{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Comparison and Error Analysis\n",
    "\n",
    "This notebook provides comprehensive comparison of all three anomaly detection models and detailed error analysis.\n",
    "\n",
    "**Models Compared:**\n",
    "1. **Mean Absolute Deviation (MAD)** - Rule-based heuristic\n",
    "2. **Vector Autoregression (VAR)** - Time series modeling\n",
    "3. **Fourier Transform** - Frequency domain analysis\n",
    "\n",
    "**Focus Areas:**\n",
    "- Model performance comparison\n",
    "- False Positive analysis (why normal data classified as anomalies)\n",
    "- False Negative analysis (why anomalies missed)\n",
    "- Visual comparison with green (correct) vs red (predicted) anomalies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score\n",
    "import pickle\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "plt.style.use('default')\n",
    "sns.set_palette(\"husl\")\n",
    "plt.rcParams['figure.figsize'] = (15, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset shape: (2000, 141)\n",
      "Original anomaly rate: 0.110\n",
      "Binary anomaly rate: 0.110\n",
      "Class distribution: {1: 1780, 2: 192, 3: 6, 4: 18, 5: 4}\n",
      "All model results loaded successfully!\n"
     ]
    }
   ],
   "source": [
    "# Load data and results\n",
    "df = pd.read_csv('../data/ECG5000_balanced.csv')\n",
    "X = df.drop('target', axis=1)\n",
    "y = df['target']\n",
    "\n",
    "print(f\"Dataset shape: {df.shape}\")\n",
    "\n",
    "# Convert multiclass to binary: 1 = normal, 2-5 = anomalies\n",
    "y_binary = (y != 1).astype(int)  # 0 for normal (class 1), 1 for anomalies (classes 2-5)\n",
    "print(f\"Original anomaly rate: {(y != 1).mean():.3f}\")\n",
    "print(f\"Binary anomaly rate: {y_binary.mean():.3f}\")\n",
    "print(f\"Class distribution: {y.value_counts().sort_index().to_dict()}\")\n",
    "\n",
    "# Load model results\n",
    "with open('../results/rule_based_results.pkl', 'rb') as f:\n",
    "    rule_results = pickle.load(f)\n",
    "with open('../results/var_model_results.pkl', 'rb') as f:\n",
    "    var_results = pickle.load(f)\n",
    "with open('../results/fourier_model_results.pkl', 'rb') as f:\n",
    "    fourier_results = pickle.load(f)\n",
    "\n",
    "print(\"All model results loaded successfully!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aligned data length: 1999\n",
      "Actual anomalies: 220\n",
      "MAD detected: 227\n",
      "VAR detected: 200\n",
      "Fourier detected: 300\n"
     ]
    }
   ],
   "source": [
    "# Extract predictions and align them\n",
    "mad_pred = rule_results['predictions']\n",
    "var_pred = var_results['predictions']\n",
    "fourier_pred = fourier_results['predictions']\n",
    "\n",
    "# Align all predictions to same length\n",
    "min_length = min(len(mad_pred), len(var_pred), len(fourier_pred), len(y_binary))\n",
    "y_aligned = y_binary.iloc[:min_length]\n",
    "mad_pred_aligned = mad_pred[:min_length]\n",
    "var_pred_aligned = var_pred[:min_length]\n",
    "fourier_pred_aligned = fourier_pred[:min_length]\n",
    "\n",
    "print(f\"Aligned data length: {min_length}\")\n",
    "print(f\"Actual anomalies: {y_aligned.sum()}\")\n",
    "print(f\"MAD detected: {mad_pred_aligned.sum()}\")\n",
    "print(f\"VAR detected: {var_pred_aligned.sum()}\")\n",
    "print(f\"Fourier detected: {fourier_pred_aligned.sum()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Comprehensive Model Performance:\n",
      "     Model  Precision  Recall  F1-Score     AUC  Accuracy   TP   FP    TN   FN\n",
      "0      MAD     0.4361  0.4500    0.4430  0.6890    0.8754   99  128  1651  121\n",
      "1      VAR     0.1050  0.0955    0.1000  0.4974    0.8109   21  179  1600  199\n",
      "2  Fourier     0.3833  0.5227    0.4423  0.7094    0.8549  115  185  1594  105\n"
     ]
    }
   ],
   "source": [
    "# Calculate comprehensive performance metrics\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "\n",
    "models = {\n",
    "    'MAD': mad_pred_aligned,\n",
    "    'VAR': var_pred_aligned,\n",
    "    'Fourier': fourier_pred_aligned\n",
    "}\n",
    "\n",
    "performance_results = []\n",
    "for name, predictions in models.items():\n",
    "    precision, recall, f1, _ = precision_recall_fscore_support(y_aligned, predictions, average='binary')\n",
    "    auc = roc_auc_score(y_aligned, predictions)\n",
    "    accuracy = (predictions == y_aligned).mean()\n",
    "    \n",
    "    tn, fp, fn, tp = confusion_matrix(y_aligned, predictions).ravel()\n",
    "    \n",
    "    performance_results.append({\n",
    "        'Model': name,\n",
    "        'Precision': precision,\n",
    "        'Recall': recall,\n",
    "        'F1-Score': f1,\n",
    "        'AUC': auc,\n",
    "        'Accuracy': accuracy,\n",
    "        'TP': tp,\n",
    "        'FP': fp,\n",
    "        'TN': tn,\n",
    "        'FN': fn\n",
    "    })\n",
    "\n",
    "performance_df = pd.DataFrame(performance_results)\n",
    "print(\"\\nComprehensive Model Performance:\")\n",
    "print(performance_df.round(4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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BA1S/fn2tW7dOo0eP1unTp3O0cktmvuOScv69NIAsMIB7yOLFiw1Jxo8//micP3/e+Pvvv43PP//cKFeunFGiRAnjn3/+MU6ePGnY2toab775ptW++/fvN+zs7KzKW7ZsaUgy5s2bl+ZcwcHBRqlSpazKIiMjDUnGwIEDrcpHjRplSDI2bdpkKfP19TUkGWvXrrVqu3nzZkOS8eCDDxqJiYmW8meeecYwmUxGUFCQVXt/f3/D19fXquz69etp+hsYGGjcf//9VmWpfdi6daul7Ny5c4ajo6MxcuRIS9n48eMNScbXX3+d5rhms9kwDMP45JNPDBsbG+Pnn3+2qp83b54hydi2bVuafW916/Xs2rWr0aZNG8MwDCMlJcXw9vY2Jk2aZJw4ccKQZEydOtWy3xtvvGGUKlXKOHz4sNXxXnrpJcPW1taIiooyDMMwzp8/b0gyJkyYkO65JRkvvfRSunW3Xt9NmzYZkoxhw4bd8VrMmDHDkGScP38+wzHfbsSIEYYk47fffstU+44dOxoODg7GsWPHLGVnzpwxypQpY7Ro0cJSlvp7ERgYaOmjYdx875hMJuP555+3lCUnJxv33Xef0bJlS0tZ6nVP/R1KtXPnTkOSMWLECEtZeu+9zz77LM37bMKECYYk45lnnknTPrUuVWau58yZMw1JxqeffmopS0xMNPz9/Y3SpUsbcXFxVmMpV66ccfHiRUvbb7/91pBkfPfdd3c8BwAUVbGxsYYk46mnnspU++zEM3eLJdL7DDeMtJ+zqbLzWZB6jsWLF1vK6tevb3h6ehr//vuvpez33383bGxsjD59+qQ5X//+/a2O2alTJ6NcuXJ3POet48hOHBMfH2+kpKSkGYejo6Px+uuvW8p2796dZmypMopXW7ZsafWZbhiGsW7dOkOSMXnyZOP48eNG6dKljY4dO951jFnl6+trdOjQwUhOTja8vb2NN954wzAMw/jzzz8NScZPP/1kiVF2795tte+XX35pSDKOHDliGIZhxMXFGU5OTsaMGTOs2t3pfXWrp556ypBkxMbG5u4AAQD3NOIr4qtb5Vd8NWfOHEOSsW7dOktZSkqKUaFCBcPf39+qbVbjqUmTJhnnz583oqOjjZ9//tlo3LixIclYsWJFro8DAFB8ESMRI90qP+9BSUqzpX4fuHLlSks/btW1a1fDZDIZR48eNQwj/Z9rqtu/X8zKd1y59b00gMxhiT3ckwICAuTh4aGKFSuqR48eKl26tL755htVqFBBX3/9tcxms7p166YLFy5YNm9vb1WtWlWbN2+2Opajo6P69euXqfOuWbNGkhQaGmpVnvq01u3rKleuXPmOS4v06dPH6qn6Jk2ayDAMSyb4reV///23kpOTLWUlSpSw/Ds2NlYXLlxQy5Ytdfz4ccXGxlrtX6tWLUsWuCR5eHioevXqOn78uKXsq6++Ur169dSpU6c0/UydEWjFihWqWbOmatSoYXVdU5c3vP26ZqRnz57asmWLoqOjtWnTJkVHR99xeb0VK1aoefPmKlu2rNV5AwIClJKSoq1bt2b6vC+88MJd23z11VcymUyaMGFCmrrUa5Ga6f3tt99alvnLjLi4OElSmTJl7to2JSVF69evV8eOHXX//fdbysuXL6+ePXvql19+sRwv1YABA6xmcEp9Tw0YMMBSZmtrq0aNGln9/FN17NhRFSpUsLx++OGH1aRJE8v7XrJ+78XHx+vChQt65JFHJEl79+5Nc8znn3/+rmPNzPVcs2aNvL299cwzz1jK7O3tNWzYMF29elU//fSTVfvu3btbPQWZ+juQ3rgBoKjLyueLlPV4JjOxRE5l57P17NmzioyMVN++feXm5mYpr1u3rv7zn/9YfX6luv1zqXnz5vr333/TfKZmJCtxjKOjo2xsbv7JlpKSon///dcydXt6n5t3kpV4tW3btnruuef0+uuvq3PnznJyctL8+fMzfa6ssrW1Vbdu3fTZZ59JurnUS8WKFa3eM7cLDw9Xo0aN9MADD0i6+d7t0KFDlpfZk2R5UvTKlSvZ6D0AAOkjviK+ulV+xVfdu3eXvb291TJ7P/30k06fPp3u8npZiacmTJggDw8PeXt7W2aRf/fdd9W1a9dcHwcAoPgiRiJGulV+3oNq0qSJNmzYYLWlrpKyZs0a2draatiwYVb7jBw5UoZh6Icffsj2eTPzHVdefi8NIC0SpHBPmjNnjjZs2KDNmzfrzz//1PHjxy2JSEeOHJFhGKpatao8PDystoMHD+rcuXNWx6pQoYIcHBwydd5Tp07JxsbGcvMhlbe3t1xdXXXq1Cmr8sqVK9/xWJUqVbJ67eLiIkmqWLFimnKz2WyV+LRt2zYFBARY1jn28PCwrFF7e4LU7eeRbk6ffenSJcvrY8eO6cEHH7xjX6Wb1/XAgQNprmm1atUkKc11zUj79u1VpkwZLV++XOHh4WrcuHGaa3rredeuXZvmvAEBAVk6r52dne677767tjt27Jh8fHysgtzbde/eXU2bNtXAgQPl5eWlHj166IsvvrhrMO3s7Cwpc1/gnT9/XtevX7daljFVzZo1ZTab06xdnJX31K0//1RVq1ZNU1atWjXLtOvSzXWshw8fLi8vL5UoUUIeHh6W9/nt7z0p49+BVJm5nqdOnVLVqlUtAX6qmjVrWupvdfu1SE2WSm/cAFDUZeXzRcp6PJOZWCKnsvPZmtrPO31WXrhwQdeuXbMqz43Ph6zEMWazWTNmzFDVqlXl6Ogod3d3eXh4aN++fel+bt5JVuJVSZo2bZrc3NwUGRmp9957T56ennfd5/z584qOjrZsV69ezfT5evbsqT///FO///67li1bph49eqRZdjfV5cuXtWbNGrVs2VJHjx61bE2bNtWePXt0+PDhTJ9XkqWfmb05CwBAZhBfEV/dLj/iq3LlyikwMFDffPON4uPjJd1cSsfOzk7dunWztMtOPDV48GBt2LBB3333nUaMGKEbN24oJSUl0+MHAEAiRiJGSiu/7kG5u7srICDAakudXODUqVPy8fFJc2/oTt8fZUVmvuPKy++lAaRlV9AdAArCww8/rEaNGqVbZzabZTKZ9MMPP8jW1jZN/e3rEd86I05m3ekLn9tldOz0+pZRufH/a9seO3ZMbdq0UY0aNTR9+nRVrFhRDg4OWrNmjWbMmJEmiLvb8TLLbDarTp06mj59err1tyfhZMTR0VGdO3fW0qVLdfz4cU2cODHD8/7nP//RmDFj0q1PTdDKzDlvT67JrhIlSmjr1q3avHmzvv/+e61du1bLly/XY489pvXr19/xmteoUUOStH//ftWvXz9X+nKrrLynsvrzT9WtWzdt375do0ePVv369VW6dGmZzWa1a9cu3T8gMvP7ld3rmZHcet8DQFHg7OwsHx8f/fHHH1naL7PxTE7+T73TOW7/MiYvPgvSkxufD1mJY9566y299tpr6t+/v9544w25ubnJxsZGL774YpZmocxqvPrbb79Zbr7s37/fagbGO2ncuLHVDaMJEyZkOLZbNWnSRFWqVNGLL76oEydO3PFpRunm7KAJCQl699139e6776apDw8P16RJkzJ1Xkn6448/5OnpablJCwBAbiC+yjziqzvLTnz17LPPavXq1Vq9erWefPJJffXVV2rbtq08PDwsbbITT1WtWtXysOHjjz8uW1tbvfTSS2rduvUd77ECAHA7YqTMI0a6s5zcg8qJzL5HbpWZ65Ef30sD+B8SpIDbVKlSRYZhqHLlyplOnsksX19fmc1mHTlyxJJ5LEkxMTG6fPmyfH19c/V86fnuu++UkJCgVatWWWWgZ2WJu9tVqVLlrgFtlSpV9Pvvv6tNmzaZDmYz0rNnTy1atEg2Njbq0aNHhue9evWq5SbOneRGn1LPt27dOl28eDHDWaRsbGzUpk0btWnTRtOnT9dbb72lV155RZs3b75jX4OCgmRra6tPP/1UvXv3zrAfHh4eKlmypA4dOpSm7q+//pKNjU2WktIy48iRI2nKDh8+LD8/P0k3n2zYuHGjJk2apPHjx2e4X1bd7Xr6+vpq3759MpvNVoluf/31lyTly+8eABRmjz/+uBYsWKAdO3bI398/w7b5Gc+ULVtWly9fTlOe3pNbWf1sTe3nnT4r3d3dVapUqZwPIh2ZjWO+/PJLtW7dWgsXLrQqv3z5stzd3S2vcyuOkaRr166pX79+qlWrlh599FFNmTJFnTp1UuPGjTPcLzw8XDdu3LC8vnWJ38x45plnNHnyZNWsWTPDRPDw8HA9+OCD6S5nPH/+fC1btizTCVI7duzQsWPH9Oyzz2aprwAAZAbxlTXiq/yJr5588kmVKVNGy5Ytk729vS5dupTu8no5jadeeeUVffjhh3r11Ve1du3au/YLAIBUxEjWiJHy/x7U7Xx9ffXjjz/qypUrVrNI3f79UeosXre/T3Iyw5SUt99LA0iLJfaA23Tu3Fm2traaNGlSmkxswzD077//ZvvY7du3lyTNnDnTqjx1VqUOHTpk+9iZlZp9fOvYYmNjtXjx4mwfs0uXLvr999/1zTffpKlLPU+3bt10+vRpffjhh2na3LhxI830oXfTunVrvfHGG3r//ffl7e19x3bdunXTjh07tG7dujR1ly9fVnJysiSpZMmSlrKc6NKliwzDSPdGUuq1uHjxYpq61C8CExIS7njsihUratCgQVq/fr1mz56dpt5sNuvdd9/VP//8I1tbW7Vt21bffvut1RJ3MTExWrZsmZo1a5brMyWsXLlSp0+ftrzetWuXdu7cqaCgIEnpv/ektL8PWZWZ69m+fXtFR0dr+fLlljbJycmaPXu2SpcurZYtW+aoDwBQ1I0ZM0alSpXSwIEDFRMTk6b+2LFjmjVrlqT8jWeqVKmi2NhY7du3z1J29uzZNDFHdj5by5cvr/r162vp0qVWn/9//PGH1q9fbxlnXshsHGNra5vmc3PFihVWn7eSLDfRchrHSNLYsWMVFRWlpUu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IiIiIiIiIiIiIiIiIiOibxQ5SRERERERERERERERERERERET0zWIHKSIiIiIiIiIiIiIiIiIiIiIi+maxgxQREREREREREREREREREREREX2z2EGKiIiIiIiIiIiIiIiIiIiIiIi+WewgRURERERERERERERERERERERE3yx2kCIiIiIiIiIiIiIiIiIiIiIiom8WO0gRFQEymQwhISGFHcZXoXHjxmjcuHFhh0HfmJCQEMhkssIOg4iIiL5ibKcSEVFBye19pLt370Imk2HFihV5HhP9d7DNQ0REREREXyt2kKKvwoIFCyCTyVCnTp3CDqXIO3r0KGQymbQoFAqYm5ujcePGmDx5Mp49e5brsh89eoSQkBBERETkXcBZiIyMREhICO7evZuv9eRGbGwshg4dCnt7e+jo6EBXVxeOjo6YOHEi4uLiCjs8AMCaNWswe/bsz9omPT0dy5cvR+PGjWFiYgKFQoHSpUuja9euOH/+fP4ESkREVABWrFih0jb6cPnzzz8LO0QVH7blPrYUNa9evUJoaCiqVasGPT09aGtr47vvvsOIESPw6NGjwg6PiIgoT73fxjh58mSm9UIIWFlZQSaToWXLloUQYd7YvXs3ZDIZLC0toVQqCzucr9rp06cREhKS4/tHAQEBkMlkMDAwQGJiYqb1N2/elM7Bn3/++bPjKaj7fEREREREREWFemEHQJQT4eHhKF26NP766y/cunULZcuWLeyQirxBgwahVq1aSE9Px7Nnz3D69GkEBwdj5syZ2LBhA5o0afLZZT569AihoaEoXbo0HBwc8j7o/xcZGYnQ0FA0btwYpUuXVlm3f//+fKv3U86dO4cWLVogISEBXbp0gaOjIwDg/PnzmDJlCo4fP16o8WVYs2YN/v77bwwePDhH+RMTE+Ht7Y29e/eiUaNG+Omnn2BiYoK7d+9iw4YNWLlyJWJiYlCqVKn8DbwQjRkzBiNHjizsMIiIKB+NHz8etra2mdKLWruyYsWKWLVqlUraqFGjoKenh9GjRxdSVJ92584duLq6IiYmBu3bt0evXr2gqamJK1euYNmyZdi6dSv++eefwg4zXxWFdiARERU8LS0trFmzBg0aNFBJP3bsGB48eACFQlFIkeWNjHtyd+/exeHDh+Hq6lrYIX21Tp8+jdDQUAQEBMDIyChH26irq+Pt27fYsWMHfHx8VNaFh4dDS0sLSUlJuYont/f52OYhIiIiIqKvFTtIUZEXHR2N06dPY8uWLejduzfCw8MRHBxc2GEVeQ0bNkS7du1U0i5fvozmzZujbdu2iIyMRIkSJQoputzT1NQslHrj4uLQpk0bqKmp4dKlS7C3t1dZP2nSJCxdurRQYvtSw4YNw969ezFr1qxMnaqCg4Mxa9aswgmsALx58wa6urpQV1eHujoviURE3zIPDw/UrFnzs7ZJS0uDUqnMsv2RcQ3JLSEEkpKSoK2trZJubm6OLl26qKRNmTIFxYsXz5ReVKSlpcHb2xuxsbE4evRoph+IJ02ahKlTpxZSdPnv7du30NHRKbR2KhERFa4WLVpg48aNmDt3rsr3yjVr1sDR0RHPnz8vxOi+zJs3b7B9+3aEhYVh+fLlCA8Pz/MOUl/apvrWKRQK1K9fH2vXrs3UQWrNmjXw9PTE5s2bCyQWtnmIiIiIiOhrxyn2qMgLDw+HsbExPD090a5dO4SHh2fKc/fuXWk46SVLlsDOzg4KhQK1atXCuXPnMuU/fPgwGjZsCF1dXRgZGaFVq1a4fv26Sp6QkBDIZDL8888/6NKlCwwNDWFqaoqxY8dCCIH79++jVatWMDAwgIWFBWbMmKGyfUpKCsaNGwdHR0cYGhpCV1cXDRs2xJEjRz66v0eOHIFMJsPWrVszrVuzZg1kMhnOnDmTk0OXSbVq1TB79mzExcXhl19+UVn38OFDdOvWDebm5lAoFKhcuTJ+++03af3Ro0dRq1YtAEDXrl2lIbxXrFgh5Tl79izc3d1haGgIHR0dODs749SpU5niePjwIbp37w5LS0soFArY2tqib9++SElJwYoVK9C+fXsAgIuLi1TP0aNHAQCNGzdG48aNVcp7+vQpunfvDnNzc2hpaaFatWpYuXKlSp7PPUc+tHjxYjx8+BAzZ87M1DkKePdj5pgxY1TSFixYgMqVK0OhUMDS0hL9+/fPNIx66dKlERAQkKm8D/czY7qdDRs2YNKkSShVqhS0tLTQtGlT3Lp1S2W7Xbt24d69e9Kx+3AUrvc9ePAAixcvRrNmzbIccUpNTQ1Dhw5VGT3q0qVL8PDwgIGBAfT09NC0adNM0xNlTDVw8uRJDBo0CKampjAyMkLv3r2RkpKCuLg4+Pn5wdjYGMbGxhg+fDiEENL2779fs2bNgo2NDbS1teHs7Iy///5bpa4rV64gICAAZcqUgZaWFiwsLNCtWze8ePFCJV/GZzoyMhKdO3eGsbGx9ANuxrr3HThwAA0aNICRkRH09PRQoUIF/PTTTyp5CuLcIyKigvH+/9ezZ8+W/r/OmPo3u2tIWloaJkyYIOUvXbo0fvrpNaod8wABAABJREFUJyQnJ6uUX7p0abRs2RL79u1DzZo1oa2tjcWLF392nEIIlC5dGq1atcq0LikpCYaGhujduzeA/7Uf1q9fj59++gkWFhbQ1dXF999/j/v372faPqdtuQ9t3rwZly9fxujRozN1jgIAAwMDTJo0SSVt48aNcHR0hLa2ttT56+HDhyp5AgICoKenh5iYGLRs2RJ6enooWbIk5s+fDwC4evUqmjRpAl1dXdjY2GDNmjUq22e0R44fP47evXujWLFiMDAwgJ+fH/7991+VvNu3b4enp6fUPrWzs8OECROQnp6ukq9x48b47rvvcOHCBTRq1Ag6OjpS+yCrduq8efNQuXJl6OjowNjYGDVr1swU5+e0rU6dOoWgoCCYmppCV1cXbdq0+aIptImI6Mt16tQJL168wIEDB6S0lJQUbNq0CZ07d85ymzdv3mDIkCGwsrKCQqFAhQoV8PPPP6t8LwaA5ORkBAYGwtTUFPr6+vj+++/x4MGDLMv81H2d3Ni6dSsSExPRvn17dOzYEVu2bMlytKLExEQMGjQIxYsXl+J8+PAhZDIZQkJCpHwfa1MBwOrVq6X2gYmJCTp27JjrNsuX3tcD3h3/4OBglC1bFgqFAlZWVhg+fHimdp5MJsOAAQOwbds2fPfdd9Lx37t3r0o8w4YNAwDY2tpK92zu3r37yfehc+fO2LNnj8o9pXPnzuHmzZtZnmMvX77E0KFDUaVKFejp6cHAwAAeHh64fPmylOdT9/k+p83j7+8PLS2tTPdW3dzcYGxszKmWiYiIiIioyOBwGVTkhYeHw9vbG5qamujUqRMWLlyIc+fOSV/i37dmzRq8fv0avXv3hkwmw7Rp0+Dt7Y07d+5AQ0MDAHDw4EF4eHigTJkyCAkJQWJiIubNm4f69evj4sWLmTqTdOjQARUrVsSUKVOwa9cuTJw4ESYmJli8eDGaNGmCqVOnIjw8HEOHDkWtWrXQqFEjAMCrV6/w66+/olOnTujZsydev36NZcuWwc3NDX/99Ve2Q1c3btwYVlZWCA8PR5s2bTIdCzs7Ozg5OeX6eLZr1w7du3fH/v37pR+qYmNjUbduXemGjqmpKfbs2YPu3bvj1atXGDx4MCpWrIjx48dj3Lhx6NWrFxo2bAgAqFevHoB3nc48PDzg6OiI4OBgyOVyLF++HE2aNMGJEydQu3ZtAO+G765duzbi4uLQq1cv2Nvb4+HDh9i0aRPevn2LRo0aYdCgQZg7dy5++uknVKxYEQCkfz+UmJiIxo0b49atWxgwYABsbW2xceNGBAQEIC4uDj/++KNK/pycI1n5448/oK2tnWlUruyEhIQgNDQUrq6u6Nu3L6KioqRz99SpUx+t62OmTJkCuVyOoUOHIj4+HtOmTYOvry/Onj0LABg9ejTi4+Px4MEDaeQnPT29bMvbs2cP0tLS8MMPP+So/mvXrqFhw4YwMDDA8OHDoaGhgcWLF6Nx48Y4duwY6tSpo5J/4MCBsLCwQGhoKP78808sWbIERkZGOH36NKytrTF58mTs3r0b06dPx3fffQc/Pz+V7X///Xe8fv0a/fv3R1JSEubMmYMmTZrg6tWrMDc3B/CuI9OdO3fQtWtXWFhY4Nq1a1iyZAmuXbuGP//8M1PHp/bt26NcuXKYPHlyppvP7+9ny5YtUbVqVYwfPx4KhQK3bt1SueFaUOceERHljfj4+EwjOMhkMhQrVkwlbfny5UhKSkKvXr2gUChgYmIircvqGtKjRw+sXLkS7dq1w5AhQ3D27FmEhYXh+vXrmTq8R0VFoVOnTujduzd69uyJChUqfPZ+yGQydOnSBdOmTcPLly9V4tuxYwdevXqVaaSpSZMmQSaTYcSIEXj69Clmz54NV1dXRERESCNY5bQtl5U//vgDAHLcnlixYgW6du2KWrVqISwsDLGxsZgzZw5OnTqFS5cuqUw5k56eDg8PDzRq1AjTpk1DeHg4BgwYAF1dXYwePRq+vr7w9vbGokWL4OfnBycnp0xTKQ4YMABGRkYICQmR2mT37t2TOpBlxKSnp4egoCDo6enh8OHDGDduHF69eoXp06erlPfixQt4eHigY8eO6NKli9Qm+dDSpUsxaNAgtGvXDj/++COSkpJw5coVnD17VvoxMzdtK2NjYwQHB+Pu3buYPXs2BgwYgPXr1+fo2BMRUd4rXbo0nJycsHbtWnh4eAB49107Pj4eHTt2xNy5c1XyCyHw/fff48iRI+jevTscHBywb98+DBs2DA8fPlQZxblHjx5YvXo1OnfujHr16uHw4cPw9PTMFENO7uvkRnh4OFxcXGBhYYGOHTti5MiR2LFjh/RgW4aAgABs2LABP/zwA+rWrYtjx45lGWeGrNpUkyZNwtixY+Hj44MePXrg2bNnmDdvHho1aqTSPvjcNktu7+splUp8//33OHnyJHr16oWKFSvi6tWrmDVrFv755x9s27ZNpZ6TJ09iy5Yt6NevH/T19TF37ly0bdsWMTExKFasGLy9vfHPP/9g7dq1mDVrFooXLw4AMDU1/eT74O3tjT59+mDLli3o1q0bgHf3GOzt7VGjRo1M+e/cuYNt27ahffv2sLW1RWxsLBYvXgxnZ2dERkbC0tLyk/f5gJy3eebMmYPDhw/D398fZ86cgZqaGhYvXoz9+/dj1apVsLS0/OQ+EhERERERFQhBVISdP39eABAHDhwQQgihVCpFqVKlxI8//qiSLzo6WgAQxYoVEy9fvpTSt2/fLgCIHTt2SGkODg7CzMxMvHjxQkq7fPmykMvlws/PT0oLDg4WAESvXr2ktLS0NFGqVCkhk8nElClTpPR///1XaGtrC39/f5W8ycnJKnH++++/wtzcXHTr1k0lHYAIDg6WXo8aNUooFAoRFxcnpT19+lSoq6ur5MvKkSNHBACxcePGbPNUq1ZNGBsbS6+7d+8uSpQoIZ4/f66Sr2PHjsLQ0FC8fftWCCHEuXPnBACxfPlylXxKpVKUK1dOuLm5CaVSKaW/fftW2NraimbNmklpfn5+Qi6Xi3PnzmWKK2PbjRs3CgDiyJEjmfI4OzsLZ2dn6fXs2bMFALF69WopLSUlRTg5OQk9PT3x6tUrIcTnnSNZMTY2FtWqVftongxPnz4Vmpqaonnz5iI9PV1K/+WXXwQA8dtvv0lpNjY2KudNdvuZ8b5WrFhR5byaM2eOACCuXr0qpXl6egobG5scxRoYGCgAiEuXLuUof+vWrYWmpqa4ffu2lPbo0SOhr68vGjVqJKUtX75cAMh0Tjg5OQmZTCb69OkjpWV8rt7f34z3S1tbWzx48EBKP3v2rAAgAgMDpbSM8/N9a9euFQDE8ePHpbSMz3SnTp0y5c9Yl2HWrFkCgHj27Fm2x6Kgzj0iIvoyGdekrBaFQiHly/j/2sDAQDx9+lSljOyuIREREQKA6NGjh0r60KFDBQBx+PBhKc3GxkYAEHv37v3sfahcubLKdTIqKkoAEAsXLlTJ9/3334vSpUtL196M9kPJkiWl65IQQmzYsEEAEHPmzBFCfF5bLivVq1cXhoaGOdqXlJQUYWZmJr777juRmJgope/cuVMAEOPGjZPS/P39BQAxefJkKS2j3S2TycS6deuk9Bs3bmRqU2e8946OjiIlJUVKnzZtmgAgtm/frrKvH+rdu7fQ0dERSUlJUpqzs7MAIBYtWpQp/4ftt1atWonKlSt/9Hh8btvK1dVV5T0KDAwUampqKt8biIioYGT833zu3Dnxyy+/CH19fel60r59e+Hi4iKEeNcG8PT0lLbbtm2bACAmTpyoUl67du2ETCYTt27dEkL8r53Rr18/lXydO3fOdM3L6X2djPbOh/d1shIbGyvU1dXF0qVLpbR69eqJVq1aqeS7cOGCACAGDx6skh4QEJApzuzaVHfv3hVqampi0qRJKulXr14V6urqUvrntFm+9L7eqlWrhFwuFydOnFCJadGiRQKAOHXqlJQGQGhqakrvnRDv7jUCEPPmzZPSpk+fLgCI6OhokRP+/v5CV1dXCPHu/GjatKkQQoj09HRhYWEhQkNDpfd0+vTp0nZJSUkq96OEePfeKxQKMX78eCktu/t8Qnxem0cIIfbt2yed13fu3BF6enqidevWOdpPIiIiIiKigsIp9qhICw8Ph7m5OVxcXAC8e2K+Q4cOWLduXabpLoB3T4UZGxtLrzOefrpz5w4A4PHjx4iIiEBAQIDK0/ZVq1ZFs2bNsHv37kxl9ujRQ/pbTU0NNWvWhBAC3bt3l9KNjIxQoUIFqZ6MvJqamgDePXX28uVLpKWloWbNmrh48eJH99vPzw/JycnYtGmTlLZ+/XqkpaVlGhEgN/T09PD69WsA755c3Lx5M7y8vCCEwPPnz6XFzc0N8fHxn4w3IiJCGtb7xYsX0vZv3rxB06ZNcfz4cSiVSiiVSmzbtg1eXl6oWbNmpnI+HOknJ3bv3g0LCwt06tRJStPQ0MCgQYOQkJCAY8eOqeT/1DmSnVevXkFfXz9HMR08eBApKSkYPHgw5PL//Tfbs2dPGBgYYNeuXTkqJytdu3aVzisg5/Fn59WrVwCQo31LT0/H/v370bp1a5QpU0ZKL1GiBDp37oyTJ09K5WXo3r27yvtap06dTJ+fjM9VVvvQunVrlCxZUnpdu3Zt1KlTR+WzmjHyBfBuaqHnz5+jbt26AJDludunT59P7mvGk6nbt2+HUqnMMk9BnXtERJQ35s+fjwMHDqgse/bsyZSvbdu22T7J/+E1JON6FBQUpJI+ZMgQAMh0zbe1tYWbm1uu9yFD+fLlUadOHZWpp1++fIk9e/bA19c3U5vKz89P5Vrfrl07lChRQoo/p2257HxOO+n8+fN4+vQp+vXrBy0tLSnd09MT9vb2WbaT3m+PZ7S7dXV14ePjI6VXqFABRkZGWV5Xe/XqpTJaY9++faGurp5te+L169d4/vw5GjZsiLdv3+LGjRsq5SkUCnTt2vWT+2pkZIQHDx5kO6VubtpWvXr1Unl/GzZsiPT0dNy7d++T8RARUf7x8fFBYmIidu7cidevX2Pnzp3ZTq+3e/duqKmpYdCgQSrpQ4YMgRBCap9kXKc+zPfhaFB5cV8nK+vWrYNcLkfbtm2ltE6dOmHPnj0qU9VmTCPXr18/le0HDhyYbdkftqm2bNkCpVIJHx8flfgtLCxQrlw5HDlyBEDu2iy5va+3ceNGVKxYEfb29ioxNWnSBACkmDK4urrCzs5Oel21alUYGBjk2Xf+zp074+jRo3jy5AkOHz6MJ0+eZHuOKRQK6X5Ueno6Xrx4AT09PVSoUOGzzoWctnkAoHnz5ujduzfGjx8Pb29vaGlp5Wo6aSIiIiIiovzEKfaoyEpPT8e6devg4uKC6OhoKb1OnTqYMWMGDh06hObNm6tsY21trfI6ozNCxo2bjB8OsprOpGLFiti3bx/evHkDXV3dbMs0NDSElpaWNBT2++kvXrxQSVu5ciVmzJiBGzduIDU1VUr/cNqPD9nb26NWrVoIDw+XbtiEh4ejbt26KFu27Ee3zYmEhATpR6xnz54hLi4OS5YswZIlS7LM//Tp04+Wd/PmTQCAv79/tnni4+ORkpKCV69e4bvvvstl5Jndu3cP5cqVU+mIBPxvSr4Pfyz61DmSHQMDA6lTWU5iAjKfZ5qamihTpswX/YCV2/izY2BgAAA52rdnz57h7du32X5+lEol7t+/j8qVK2cbr6GhIQDAysoqU3pW+1CuXLlMaeXLl8eGDRuk1y9fvkRoaCjWrVuX6VyNj4/PtP2nPn/Au85Mv/76K3r06IGRI0eiadOm8Pb2Rrt27aRzraDOPSIiyhu1a9fOsoP2hz52nfhw3b179yCXyzO1zywsLGBkZJTpWpCTa1BO+fn5YcCAAbh37x5sbGywceNGpKamZjnN3YfXU5lMhrJly+Lu3bsAct6We7+j7/s+58e/j7XH7e3tcfLkSZU0LS2tTB3WDA0NUapUqUwdwXLantDT00OJEiWk/QfeTXU3ZswYHD58OFOnpA/bEyVLllTpsJ6dESNG4ODBg6hduzbKli2L5s2bo3Pnzqhfvz6AvGlbsT1BRFQ0mJqawtXVFWvWrMHbt2+Rnp6Odu3aZZn33r17sLS0zNS5+MPvkhntjPc73QCZr6F5cV8nK6tXr0bt2rXx4sUL6X5X9erVkZKSgo0bN6JXr14qcX7YzvnY/asP8968eRNCiCzvAQCQOjrnps2S2/t6N2/exPXr17PtOP/hMf2wHuDddTqvrtEtWrSAvr4+1q9fj4iICNSqVUulPfc+pVKJOXPmYMGCBYiOjlZ5yPTD6aU/Jqdtngw///wztm/fjoiICKxZswZmZmY53paIiIiIiKggsIMUFVmHDx/G48ePsW7dOqxbty7T+vDw8EwdpNTU1LIsSwiR6ziyKjMn9axevRoBAQFo3bo1hg0bBjMzM6ipqSEsLAy3b9/+ZL1+fn748ccf8eDBAyQnJ+PPP//EL7/8kuv9yJCamop//vlH6qSU8WRdly5dsr3BVLVq1Y+WmVHG9OnT4eDgkGUePT09vHz5MpdR553cniP29vaIiIhASkrKZ90c+pTsRs1KT0/P9bn3Oezt7QEAV69ezfa9+xLZxZtVem73wcfHB6dPn8awYcPg4OAAPT09KJVKuLu7ZznaxfsjRGRHW1sbx48fx5EjR7Br1y7s3bsX69evR5MmTbB///5s9+tj8uP/JyIiynsfu05kty6no2Dm5BqUUx07dkRgYCDCw8Px008/YfXq1ahZs2aWnW0+JadtuezY29vj0qVLuH//fqZO0F/qc9oSQO6uq3FxcXB2doaBgQHGjx8POzs7aGlp4eLFixgxYkSm9kRO38eKFSsiKioKO3fuxN69e7F582YsWLAA48aNQ2ho6GfHCbA9QURUlHXu3Bk9e/bEkydP4OHhIY1MnN/y4r7Oh27evCmNgJhVp6Xw8HCpg1RufHgtVSqVkMlk2LNnT5bXuox2SG7aLLm9t6JUKlGlShXMnDkzy7wftnny+xqtUCjg7e2NlStX4s6dOwgJCck27+TJkzF27Fh069YNEyZMgImJCeRyOQYPHvzRUUE/9Llt10uXLkkdx65evaoy4jYREREREVFRwA5SVGSFh4fDzMwM8+fPz7Ruy5Yt2Lp1KxYtWvRZX9ZtbGwAAFFRUZnW3bhxA8WLF1cZPepLbNq0CWXKlMGWLVtUfjQLDg7O0fYdO3ZEUFAQ1q5di8TERGhoaKBDhw55EldiYqI0vYupqSn09fWRnp4OV1fXj26b3Y9/GU8zGhgYfLQMU1NTGBgY4O+//85VPVmxsbHBlStXoFQqVUbyyZgKJeM9/1JeXl44c+YMNm/e/MkbPO+fZ+9Pl5KSkoLo6GiVY2RsbIy4uLhMZdy7d09l28/xOcfPw8MDampqWL16dZYjTrzP1NQUOjo62X5+5HJ5nv8omvF06Pv++ecflC5dGsC70RIOHTqE0NBQjBs37qPbfS65XI6mTZuiadOmmDlzJiZPnozRo0fjyJEjcHV1LbBzj4iIii4bGxsolUrcvHlTGvUBAGJjYxEXF5ev1wITExN4enoiPDwcvr6+OHXqFGbPnp1l3g+vi0II3Lp1S/qxNKdtuex4eXlh7dq1WL16NUaNGvXRvO+3kzKmqMkQFRWVL8fs5s2b0pTdwLvRVB8/fowWLVoAAI4ePYoXL15gy5YtaNSokZTv/VFsc0tXVxcdOnRAhw4dkJKSAm9vb0yaNAmjRo0qlLYVERHlnzZt2qB37974888/sX79+mzz2djY4ODBg3j9+rXKKFIffpfMaGfcvn1bpQP0h9eNz7mvk1Ph4eHQ0NDAqlWrMnX8OXnyJObOnYuYmBhYW1tLcUZHR6t0prp161aO67Ozs4MQAra2tihfvvxH8wG5b7N8Djs7O1y+fBlNmzb9rPssH/Ol5XTu3Bm//fYb5HI5OnbsmG2+TZs2wcXFBcuWLVNJj4uLUxk5K6/2CwDevHmDrl27olKlSqhXrx6mTZuGNm3aoFatWnlWBxERERER0ZeSfzoLUcFLTEzEli1b0LJlS7Rr1y7TMmDAALx+/Rp//PHHZ5VbokQJODg4YOXKlSqdUv7++2/s379f+pEkL2TcQHr/SbGzZ8/izJkzOdq+ePHi8PDwwOrVqxEeHg53d/dMw39/rsuXL2Pw4MEwNjZG//79pTjbtm2LzZs3Z9lx6dmzZ9LfGZ3HPuzQ4+joCDs7O/z8889ISEjItgy5XI7WrVtjx44dOH/+fKZ8Gccqu3qy0qJFCzx58kTl5mNaWhrmzZsHPT09ODs7f7KMnOjTpw9KlCiBIUOG4J9//sm0/unTp5g4cSIAwNXVFZqampg7d67K+79s2TLEx8fD09NTSrOzs8Off/6JlJQUKW3nzp24f/9+rmPV1dXNcmq5rFhZWaFnz57Yv38/5s2bl2m9UqnEjBkz8ODBA6ipqaF58+bYvn27yhDusbGxWLNmDRo0aCBN2ZdXtm3bhocPH0qv//rrL5w9exYeHh4Asv6cAcj2B+Kcymq0s4ynU5OTkwEU3LlHRERFV0bb8cPrTsZIA+9f8/PDDz/8gMjISAwbNgxqamrZ/lD2+++/q0ynu2nTJjx+/Fi6nua0LZeddu3aoUqVKpg0aVKWbd3Xr19j9OjRAICaNWvCzMwMixYtkq6pALBnzx5cv349X47ZkiVLVKa7XrhwIdLS0j7ankhJScGCBQu+qN4Pp9/W1NREpUqVIIRAampqobStiIgo/+jp6WHhwoUICQmBl5dXtvlatGiB9PT0TKOEz5o1CzKZTLo+Zfw7d+5clXwftjs+575OToWHh6Nhw4bo0KFDpntyw4YNAwCsXbsWAKQH8D68bmZ1jyE73t7eUFNTQ2hoaKbv90II6Zr6pW2Wz+Hj44OHDx9i6dKlmdYlJibizZs3n13m59zvyoqLiwsmTJiAX375BRYWFtnmU1NTy3QcN27cqHJ/JS/ied+IESMQExODlStXYubMmShdujT8/f1V2ntERERERESFjSNIUZH0xx9/4PXr1/j++++zXF+3bl2YmpoiPDz8s0dVmj59Ojw8PODk5ITu3bsjMTER8+bNg6Gh4UeHp/5cLVu2xJYtW9CmTRt4enoiOjoaixYtQqVKlbK8iZMVPz8/tGvXDgAwYcKEz6r/xIkTSEpKQnp6Ol68eIFTp07hjz/+gKGhIbZu3apyI2XKlCk4cuQI6tSpg549e6JSpUp4+fIlLl68iIMHD0qdRezs7GBkZIRFixZBX18furq6qFOnDmxtbfHrr7/Cw8MDlStXRteuXVGyZEk8fPgQR44cgYGBAXbs2AHg3TDf+/fvh7OzM3r16oWKFSvi8ePH2LhxI06ePAkjIyM4ODhATU0NU6dORXx8PBQKBZo0aQIzM7NM+9mrVy8sXrwYAQEBuHDhAkqXLo1NmzZJoyi8/zTmlzA2NsbWrVvRokULODg4oEuXLnB0dAQAXLx4EWvXroWTkxOAd09vjho1CqGhoXB3d8f333+PqKgoLFiwALVq1UKXLl2kcnv06IFNmzbB3d0dPj4+uH37NlavXi09FZkbjo6OWL9+PYKCglCrVi3o6el99ObsjBkzcPv2bQwaNEjqmGhsbIyYmBhs3LgRN27ckH5wnThxIg4cOIAGDRqgX79+UFdXx+LFi5GcnIxp06blOubslC1bFg0aNEDfvn2RnJyM2bNno1ixYhg+fDiAd0+NNmrUCNOmTUNqaipKliyJ/fv3f/GID+PHj8fx48fh6ekJGxsbPH36FAsWLECpUqXQoEEDAAV37hERUd7Ys2ePNDLD++rVq5frURurVasGf39/LFmyRJqm7a+//sLKlSvRunVrlVGL8oOnpyeKFSuGjRs3wsPDI8u2EvButKkGDRqga9euiI2NxezZs1G2bFn07NkTwLtO7Dlty2VFQ0MDW7ZsgaurKxo1agQfHx/Ur18fGhoauHbtGtasWQNjY2NMmjQJGhoamDp1Krp27QpnZ2d06tQJsbGxmDNnDkqXLo3AwMA8P04pKSlo2rQpfHx8pDZZgwYNpO8a9erVg7GxMfz9/TFo0CDIZDKsWrXqi6fEad68OSwsLFC/fn2Ym5vj+vXr+OWXX+Dp6Sm1Ewq6bUVERPkruynu3ufl5QUXFxeMHj0ad+/eRbVq1bB//35s374dgwcPlu4HODg4oFOnTliwYAHi4+NRr149HDp0KMuRmXJ6Xycnzp49i1u3bmHAgAFZri9ZsiRq1KiB8PBwjBgxAo6Ojmjbti1mz56NFy9eoG7dujh27Jj0cFlORimys7PDxIkTMWrUKNy9exetW7eGvr4+oqOjsXXrVvTq1QtDhw794jbL5/jhhx+wYcMG9OnTB0eOHEH9+vWRnp6OGzduYMOGDdi3bx9q1qz5WWVm3EcaPXo0OnbsCA0NDXh5eeV4NHu5XI4xY8Z8Ml/Lli0xfvx4dO3aFfXq1cPVq1cRHh6eqc37sft8n+Pw4cNYsGABgoODUaNGDQDA8uXL0bhxY4wdO5ZtGiIiIiIiKjoEURHk5eUltLS0xJs3b7LNExAQIDQ0NMTz589FdHS0ACCmT5+eKR8AERwcrJJ28OBBUb9+faGtrS0MDAyEl5eXiIyMVMkTHBwsAIhnz56ppPv7+wtdXd1M9Tg7O4vKlStLr5VKpZg8ebKwsbERCoVCVK9eXezcuVP4+/sLGxubT8YohBDJycnC2NhYGBoaisTExGyPxfuOHDkiAEiLhoaGMDU1FY0aNRKTJk0ST58+zXK72NhY0b9/f2FlZSU0NDSEhYWFaNq0qViyZIlKvu3bt4tKlSoJdXV1AUAsX75cWnfp0iXh7e0tihUrJhQKhbCxsRE+Pj7i0KFDKmXcu3dP+Pn5CVNTU6FQKESZMmVE//79RXJyspRn6dKlokyZMkJNTU0AEEeOHBFCvDvOzs7OmWLv2rWrKF68uNDU1BRVqlRRiUsI8dnnSHYePXokAgMDRfny5YWWlpbQ0dERjo6OYtKkSSI+Pl4l7y+//CLs7e2FhoaGMDc3F3379hX//vtvpjJnzJghSpYsKRQKhahfv744f/58pv3MeF83btyY5X69v78JCQmic+fOwsjISADIdL5lJS0tTfz666+iYcOGwtDQUGhoaAgbGxvRtWtXcenSJZW8Fy9eFG5ubkJPT0/o6OgIFxcXcfr0aZU8y5cvFwDEuXPnVNJz+rl6//2aMWOGsLKyEgqFQjRs2FBcvnxZZdsHDx6INm3aCCMjI2FoaCjat28vHj16lOl9za7u99dlOHTokGjVqpWwtLQUmpqawtLSUnTq1En8888/KtsV5LlHRES5k3FNym7J+H/7Y/9ff+wakpqaKkJDQ4Wtra3Q0NAQVlZWYtSoUSIpKUkln42NjfD09MzVPlSuXDlT+ydDv379BACxZs2aTOsy2g9r164Vo0aNEmZmZkJbW1t4enqKe/fuZcqf07Zcdv79918xbtw4UaVKFaGjoyO0tLTEd999J0aNGiUeP36sknf9+vWievXqQqFQCBMTE+Hr6ysePHigkien7e4MHx7jjPf+2LFjolevXsLY2Fjo6ekJX19f8eLFC5VtT506JerWrSu0tbWFpaWlGD58uNi3b59KO/RjdWese/99Wrx4sWjUqJF0PO3s7MSwYcMytRm/pG2V8R6/HyMRERWM7P5v/lBWbYDXr1+LwMBAYWlpKTQ0NES5cuXE9OnThVKpVMmXmJgoBg0aJIoVKyZ0dXWFl5eXuH//fpbfJXNyXyerewgfGjhwoAAgbt++nW2ekJAQAUD6fv7mzRvRv39/YWJiIvT09ETr1q1FVFSUACCmTJkibfexNpUQQmzevFk0aNBA6OrqCl1dXWFvby/69+8voqKiVPLlpM3ypff1hBAiJSVFTJ06VVSuXFkoFAphbGwsHB0dRWhoqMr1HIDo379/pjJtbGyEv7+/StqECRNEyZIlhVwuFwBEdHR0lsfiY7G+L6s2bFJSkhgyZIgoUaKE0NbWFvXr1xdnzpzJ8p5advf5ctrmefXqlbCxsRE1atQQqampKvkCAwOFXC4XZ86c+eg+EBERERERFRSZEF/4WCwR5Zu0tDRYWlrCy8sLy5YtK+xwiArM3bt3YWtri+nTp2Po0KGFHQ4REVGRFRgYiGXLluHJkyfQ0dFRWXf06FG4uLhg48aN0qik/yUrVqxA165dce7cuc8e4YGIiIi+TEREBKpXr47Vq1fD19e3sMMhIiIiIiIigrywAyCi7G3btg3Pnj2Dn59fYYdCREREREVMUlISVq9ejbZt22bqHEVERERUUBITEzOlzZ49G3K5HI0aNSqEiIiIiIiIiIgyUy/sAIgos7Nnz+LKlSuYMGECqlevDmdn58IOiYiIiIiKiKdPn+LgwYPYtGkTXrx4gR9//LGwQyIiIqL/sGnTpuHChQtwcXGBuro69uzZgz179qBXr16wsrIq7PCIiIiIiIiIALCDFFGRtHDhQqxevRoODg5YsWJFYYdDREREREVIZGQkfH19YWZmhrlz58LBwaGwQyIiIqL/sHr16uHAgQOYMGECEhISYG1tjZCQEIwePbqwQyMiIiIiIiKSyIQQorCDICIiIiIiIiIiIiIiIioox48fx/Tp03HhwgU8fvwYW7duRevWrT+6zdGjRxEUFIRr167BysoKY8aMQUBAgEqe+fPnY/r06Xjy5AmqVauGefPmoXbt2tL6pKQkDBkyBOvWrUNycjLc3NywYMECmJub58NeEhEREVEGeWEHQERERERERERERERERFSQ3rx5g2rVqmH+/Pk5yh8dHQ1PT0+4uLggIiICgwcPRo8ePbBv3z4pz/r16xEUFITg4GBcvHgR1apVg5ubG54+fSrlCQwMxI4dO7Bx40YcO3YMjx49gre3d57vHxERERGp4ghSRERERERERERERERE9J8lk8k+OYLUiBEjsGvXLvz9999SWseOHREXF4e9e/cCAOrUqYNatWrhl19+AQAolUpYWVlh4MCBGDlyJOLj42Fqaoo1a9agXbt2AIAbN26gYsWKOHPmDOrWrZt/O0lERET0H6de2AEUBUqlEo8ePYK+vj5kMllhh0NERESFRAiB169fw9LSEnI5B9r8XGxTEREREcA2FRERERWe5ORkJCcnq6QpFAooFIovLvvMmTNwdXVVSXNzc8PgwYMBACkpKbhw4QJGjRolrZfL5XB1dcWZM2cAABcuXEBqaqpKOfb29rC2tmYHKSIiIqJ8xg5SAB49egQrK6vCDoOIiIiKiPv376NUqVKFHcZXh20qIiIieh/bVLkXKqtQ2CHQVyD4+ahPZ6L/PHFofWGHQF8Bmc+eAqmnIK5vIrgTQkNDVdKCg4MREhLyxWU/efIE5ubmKmnm5uZ49eoVEhMT8e+//yI9PT3LPDdu3JDK0NTUhJGRUaY8T548+eIYiYiIiCh77CAFQF9fH8C7G3cGBgaFHM23S6lU4tmzZzA1NeUTpFTk8Pykoo7naMF49eoVrKyspLYBfR62qfIHP//0teE5S18Tnq/5g20qIiIiykpBtLaGjxqFoKAglbS8GD2KiIiIiL5+7CAFSFPAGBgY8Me8fKRUKpGUlAQDAwPeeKYih+cnFXU8RwsWp4fLHbap8gc///S14TlLXxOer/mLbSoiIiIqaHk1nV5WLCwsEBsbq5IWGxsLAwMDaGtrQ01NDWpqalnmsbCwkMpISUlBXFycyihS7+chIiIiovzBu39ERERERERERERERESUr+QFsOQnJycnHDp0SCXtwIEDcHJyAgBoamrC0dFRJY9SqcShQ4ekPI6OjtDQ0FDJExUVhZiYGCkPEREREeUPjiBFRERERERERERERERE/ykJCQm4deuW9Do6OhoREREwMTGBtbU1Ro0ahYcPH+L3338HAPTp0we//PILhg8fjm7duuHw4cPYsGEDdu3aJZURFBQEf39/1KxZE7Vr18bs2bPx5s0bdO3aFQBgaGiI7t27IygoCCYmJjAwMMDAgQPh5OSEunXrFuwBICIiIvqPYQepHFIqlUhJSSnsML5qSqUSqampSEpK4tQF/0Gampp834mIiIiI8hG/t34+fk/NPQ0NDaipqRV2GERERPQVKWqtrfPnz8PFxUV6HRQUBADw9/fHihUr8PjxY8TExEjrbW1tsWvXLgQGBmLOnDkoVaoUfv31V7i5uUl5OnTogGfPnmHcuHF48uQJHBwcsHfvXpibm0t5Zs2aBblcjrZt2yI5ORlubm5YsGBBAewxERER0X8bO0jlQEpKCqKjo6FUKgs7lK+aEAJKpRKvX7+GTCYr7HCogMnlctja2kJTU7OwQyEiIiIi+ubwe2vu8HvqlzEyMoKFhQWPHREREX2VGjduDCFEtutXrFiR5TaXLl36aLkDBgzAgAEDsl2vpaWF+fPnY/78+TmOlYiIiIi+HDtIfYIQAo8fP4aamhqsrKz4ROkXEEIgLS0N6urqvHn6H6NUKvHo0SM8fvwY1tbWfP+JiIiIiPIQv7fmHr+n5o4QAm/fvsXTp08BACVKlCjkiIiIiOhrwFYqERERERUmdpD6hLS0NLx9+xaWlpbQ0dEp7HC+arzx/N9mamqKR48eIS0tDRoaGoUdDhERERHRN4PfW3OP31NzT1tbGwDw9OlTmJmZcbo9IiIiIiIiIiIq0thB6hPS09MBgNOCEX2hjM9Qeno6O0gREREREeUhfm+lwpLRIS81NZUdpIiIiOiTOIIUERERERUmtkdziE+SEn0ZfoaIiIiIiPIX29xU0HjOERERERERERHR14IjSBEREREREREREREREVG+YtdqIiIiIipMHEGKiIiIiIiIiIiIiIiIiIiIiIi+WRxBioiIiIiIiOgblDxzZYHWpwjyL7C6VqxYgcGDByMuLq7A6sxLMpkMW7duRevWrbPNExAQgLi4OGzbtq3A4iIiIiLKT3xin4iIiIgKE9ujVChkMtk3f5O3dOnSmD17tvS6IPZ52bJlaN68eb7WkZW9e/fCwcEBSqWywOsmIiIiIqKvU0BAAGQyWabl1q1bhR0aVqxYIcUjl8tRqlQpdO3aFU+fPs2T8h8/fgwPDw8AwN27dyGTyRAREaGSZ86cOVixYkWe1EdERERERERERPRfxw5S37gzZ85ATU0Nnp6en73thx18CsOXxF/UvH8DPD8kJSVh7NixCA4OltKWLl2Khg0bwtjYGMbGxnB1dcVff/0lrU9NTcWIESNQpUoV6OrqwtLSEn5+fnj06JFK2S9fvoSvry8MDAxgZGSE7t27IyEhQVrv7u4ODQ0NhIeH59v+ERERERHRt8fd3R2PHz9WWWxtbQs7LACAgYEBHj9+jAcPHmDp0qXYs2cPfvjhhzwp28LCAgqF4qN5DA0NYWRklCf1ERERERUF8gJYiIiIiIiyw/Zibr15k/2SlJTzvImJOcubS8uWLcPAgQNx/PjxTJ1evgZfe/zvy8kN8C+xadMmGBgYoH79+lLa0aNH0alTJxw5cgRnzpyBlZUVmjdvjocPHwIA3r59i4sXL2Ls2LG4ePEitmzZgqioKHz//fcqZfv6+uLatWs4cOAAdu7ciePHj6NXr14qeQICAjB37tx82z8iIiIiIvr2KBQKWFhYqCxqamqYOXOm9CCHlZUV+vXrp/KQxocuX74MFxcX6Ovrw8DAAI6Ojjh//ry0/uTJk2jYsCG0tbVhZWWFQYMG4c0nvmvLZDJYWFjA0tISHh4eGDRoEA4ePIjExEQolUqMHz8epUqVgkKhgIODA/bu3Sttm5KSggEDBqBEiRLQ0tKCjY0NwsLCVMrOGGE4o0NY9erVIZPJ0LhxYwDvvmNlTMG3ZMkSWFpaZhq1t1WrVujWrZv0evv27ahRowa0tLRQpkwZhIaGIi0tDQAghEBISAisra2hUChgaWmJQYMGffQYEBERERERERERfSvYQSq39PSyX9q2Vc1rZpZ93g9HFCpdOut8uZCQkID169ejb9++8PT0zHJo/h07dqBWrVrQ0tJC8eLF0aZNGwBA48aNce/ePQQGBkrTCgBASEgIHBwcVMqYPXs2SpcuLb0+d+4cmjVrhuLFi8PQ0BDOzs64ePFinsd/9OhRyGQyHDp0CDVr1oSOjg7q1auHqKgolXwLFy6EnZ0dNDU1UaFCBaxatUplvUwmw+LFi9GyZUvo6OigYsWKOHPmDG7duoXGjRtDV1cX9erVw+3bt6Vtbt++jVatWsHc3Bx6enqoVasWDh48+NH9+XCKvfv378PHxwdGRkYwMTFBq1atcPfuXZX9q127NnR1dWFkZIT69evj3r172Za/bt06eHl5qaSFh4ejX79+cHBwgL29PX799VcolUocOnQIwLsnkg8cOAAfHx9UqFABdevWxS+//IILFy4gJiYGAHD9+nXs3bsXv/76K+rUqYMGDRpg3rx5WLdunUqnNS8vL5w/f17lOBEREREREeWGXC7H3Llzce3aNaxcuRKHDx/G8OHDs83v6+uLUqVK4dy5c7hw4QJGjhwJDQ0NAO++v7m7u6Nt27a4cuUK1q9fj5MnT2LAgAGfFZO2tjaUSiXS0tIwZ84czJgxAz///DOuXLkCNzc3fP/997h58yYAYO7cufjjjz+wYcMGREVFITw8XOV78/syRvk9ePAgHj9+jC1btmTK0759e7x48QJHjhyR0l6+fIm9e/fC19cXAHDixAn4+fnhxx9/RGRkJBYvXowVK1Zg0qRJAIDNmzdj1qxZWLx4MW7evIlt27ahSpUqn3UMiIiIiL4ER5AiIiIiosLE9uI3bMOGDbC3t0eFChXQpUsX/PbbbxBCSOt37dqFNm3aoEWLFrh06RIOHTqE2rVrAwC2bNmCUqVKYfz48dI0Bzn1+vVr+Pv74+TJk/jzzz9Rrlw5tGjRAq9fv87T+DOMHj0aM2bMwPnz56Gurq7y9OzWrVvx448/YsiQIfj777/Ru3dvdO3aVeWmMgBMmDABfn5+iIiIgL29PTp37ozevXtj1KhROH/+PIQQKjfPExIS0KJFCxw6dAiXLl2Cu7s7vLy8pE5Fn5Kamgo3Nzfo6+vjxIkTOHXqFPT09ODu7o6UlBSkpaWhdevWcHZ2xpUrV3DmzBn06tVL6qiWlZMnT6JmzZofrfft27dITU2FiYlJtnni4+Mhk8mkqRzOnDkDIyMjlbJdXV0hl8tx9uxZKc3a2hrm5uY4ceJEjo4BERERERHRzp07oaenJy3t27cHAAwePBguLi4oXbo0mjRpgokTJ2LDhg3ZlhMTEwNXV1fY29ujXLlyaN++PapVqwYACAsLg6+vLwYPHoxy5cqhXr16mDt3Ln7//XckfTgCdDZu3ryJRYsWoWbNmtDX18fPP/+MESNGoGPHjqhQoQKmTp0KBwcHaZr6mJgYlCtXDg0aNICNjQ0aNGiATp06ZVm2qakpAKBYsWKwsLDI8vuasbExPDw8sGbNGilt06ZNKF68OFxcXAAAoaGhGDlyJPz9/VGmTBk0a9YMEyZMwOLFi6WYLCws4OrqCmtra9SuXRs9e/bM0f4TERERERERERF97dQLO4Cv1keG9oeamurrp0+zzyv/oI/aeyMIfally5ahS5cuAAB3d3fEx8fj2LFj0nD9kyZNQseOHREaGiptk3ED2cTEBGpqatDX14eFhcVn1dukSROV10uWLIGRkRGOHTsGd3f3PIs/w6RJk+Ds7AwAGDlyJDw9PZGUlAQtLS38/PPPCAgIQL9+/QAAQUFB+PPPP/Hzzz9LN5EBoGvXrvDx8QEAjBgxAk5OThg7dizc3NwAAD/++CO6du0q5a9WrZp0rIB3Hay2bt2KP/74I0dPIa9fvx5KpRK//vqr1Olp+fLlMDIywtGjR1GzZk3Ex8ejZcuWsLOzAwBUrFgx2/Li4uIQHx8PS0vLj9Y7YsQIWFpawtXVNcv1SUlJGDFiBDp16gQDAwMAwJMnT2BmZqaST11dHSYmJnjy5IlKuqWl5UdHuSIiIiIiInqfi4sLFi5cKL3W1dUF8G40pbCwMNy4cQOvXr1CWloakpKS8PbtW+jo6GQqJygoCD169MCqVavg6uqK9u3bS9+lLl++jCtXriA8PFzKL4SAUqlEdHQ0ypUrl2Vs8fHx0NPTg1KpRFJSEho0aIBff/0Vr169wqNHj1SmNweA+vXr4/LlywDeTY/XrFkzVKhQAe7u7mjZsiWaN2/+RcfK19cXPXv2xIIFC6BQKBAeHo6OHTtC/v/3FS5fvoxTp05JI0YBQHp6unTc2rdvj9mzZ6NMmTJwd3dHixYt4OXlBXV13hoiIiKigsEn9omIiIioMBVqe3ThwoWoWrUqDAwMYGBgACcnJ+zZs0dan5SUhP79+6NYsWLQ09ND27ZtERsbq1JGTEwMPD09oaOjAzMzMwwbNgxpaWn5H7yubvaLllbO82pr5yzvZ4qKisJff/0lPaGqrq6ODh06YNmyZVKeiIgING3a9LPL/pTY2Fj07NkT5cqVg6GhIQwMDJCQkJDj0ZVyGn+GqlWrSn+XKFECAPD0/zulXb9+Pcub1tevX8+2DHNzcwBQmWrA3NwcSUlJePXqFYB3I0gNHToUFStWhJGREfT09HD9+vUc7+Ply5dx69Yt6OvrS09Km5iYICkpCbdv34aJiQkCAgLg5uYGLy8vzJkz56OjeCUmJgIAtD48994zZcoUrFu3Dlu3bs0yX2pqKnx8fCCEUPmB4nNoa2vj7du3udqWiIiIiIj+e3R1dVG2bFlpKVGiBO7evYuWLVuiatWq2Lx5My5cuID58+cDAFJSUrIsJyQkBNeuXYOnpycOHz6MSpUqYevWrQDefX/r3bs3IiIipOXy5cu4efOm1IkqK/r6+oiIiMDff/+NN2/e4Pjx4yhfvnyO9qtGjRqIjo7GhAkTkJiYCB8fH7Rr1+4zj44qLy8vCCGwa9cu3L9/HydOnJCm18vYz9DQUJX9vHr1Km7evAktLS1YWVkhKioKCxYsgLa2Nvr164dGjRohNTX1i+IiIiIiIiIiIiL6GhTqY4KlSpXClClTUK5cOQghsHLlSrRq1QqXLl1C5cqVERgYiF27dmHjxo0wNDTEgAED4O3tjVOnTgF49ySkp6cnLCwscPr0aTx+/Bh+fn7Q0NDA5MmTC3PXCt2yZcuQlpamMqKQEAIKhQK//PILDA0Nof1h56wckMvlmaa5+/Bmqr+/P168eIE5c+bAxsYGCoUCTk5O2d7Izm38GTQ0NKS/M0ZjUiqVn7VfWZXxsXKHDh2KAwcO4Oeff0bZsmWhra2Ndu3a5XgfExIS4OjoqPIEc4aM6RWWL1+OQYMGYe/evVi/fj3GjBmDAwcOoG7dupm2KVasGGQyGf79998s6/v5558xZcoUHDx4UKUzWIaMzlH37t3D4cOHpdGjAMDCwkLqcJYhLS0NL1++zDS62MuXL6X4iYiIiIiIcuPChQtQKpWYMWOGNDrSx6bXy1C+fHmUL18egYGB6NSpE5YvX442bdqgRo0aiIyMRNmyZTNtI4TI9iEruVye5TYGBgawtLTEqVOnpNGMAeDUqVPStPUZ+Tp06IAOHTqgXbt2cHd3x8uXLzNNoaepqQng3T2Oj9HS0oK3tzfCw8Nx69YtVKhQATVq1JDW16hRA1FRUVnGnEFbWxteXl7w8vJC//79YW9vj6tXr6qUQ0RERJRfOIIUERERERWmQu0g5eXlpfJ60qRJWLhwIf7880+UKlUKy5Ytw5o1a6Qp25YvX46KFSvizz//RN26dbF//35ERkbi4MGDMDc3h4ODAyZMmIARI0YgJCREusn4oeTkZCQnJ0uvM0YFUiqVmTrWKJVKCCGk5WuQlpaG33//HT///HOmIfzbtGmDNWvWoE+fPqhatSoOHTqEgICALMvR1NREWlqayn4XL14cT548gVKplDoNRUREAICU79SpU5g/fz48PDwAAPfv38fz589V8mT8ndUxzWn8Gdu+X86HaRUrVsTJkyfh5+cnlXHq1ClUqlQp21hyUu6pU6fg7++P1q1bA3jX4enu3buZ9im719WrV8f69ethamqq0hnp/XwA4ODgAAcHB4wcORL16tVDeHg46tSpkym/hoYGKlWqhGvXrqFZs2Yq66ZNm4bJkydj7969cHR0zLKDW4cOHXDz5k0cPnwYJiYmKnnq1q2LuLg4nD9/Ho6OjgCAQ4cOQalUonbt2lLejNGvHBwcsnxfM/Y9q89ZUZDxWS+KsREBPEcLCo8vERFR4StbtixSU1Mxb948eHl54dSpU1i0aFG2+RMTEzFs2DC0a9cOtra2ePDgAc6dO4e2bdsCeDfVeN26dTFgwAD06NEDurq6iIyMxIEDBzBv3rxcxThs2DAEBwfDzs4ODg4OWL58OSIiIqSHYGbOnIkSJUqgevXqkMvl2LhxIywsLGBkZJSpLDMzM2hra2Pv3r0oVaoUtLS0VB4Kep+vry9atmyJa9euSVPSZxg3bhxatmwJa2trtGvXDnK5HJcvX8bff/+NiRMnYsWKFUhPT0edOnWgo6OD1atXQ1tbGzY2Nrk6BkRERERERERERF+TQu0g9b709HRs3LgRb968gZOTEy5cuIDU1FS4urpKeezt7WFtbY0zZ86gbt26OHPmDKpUqSJNiQYAbm5u6Nu3L65du4bq1atnWVdYWBhCQ0MzpT979gxJSUkqaampqVAqlUhLSyuYqfvywPbt2/Hvv//C398/003V1q1bY9myZejRowdGjx4NNzc3lC5dGj4+PkhLS8PevXsxbNgwAICNjQ2OHTuGdu3aQaFQoHjx4mjQoAGePXuGKVOmwNvbG/v378eePXtgYGAgHZ+yZcti1apVcHBwwOvXrzFy5Ehoa2sjPT1d5YnY9PT0LI9pTuPPKOv99+b9f9PS0hAYGIjOnTujWrVqaNKkCXbt2oUtW7Zg7969KnW/H8uHZWSsfz/Nzs4OW7ZsgYeHB2QyGUJCQqTOC++Xm3HufFhPhw4dMH36dLRq1QrBwcEoWbIkYmJisG3bNgwZMgSpqan49ddf4eXlhRIlSuCff/7BzZs34evrm+152KxZM5w4cQIDBgyQ0qZPn47Q0FD8/vvvKFWqFB48eAAA0rR+GZ2jIiIisHXrViQnJ0t5TExMoKmpiXLlysHNzQ09e/bE/PnzkZqaigEDBsDHxwdmZmZSPKdOnYJCoUCtWrWyjDEtLQ1KpRIvXrxQGZ2rqFAqlYiPj4cQQnpKnQpf4t69hR1CkSEAvNbQQGJqKmSFHUwRoO3uni/lvn79Ol/KJSqq/A/v+XSmz7SyiUeel0lEuaMI8i/sEHKlWrVqmDlzJqZOnYpRo0ahUaNGCAsLU3nw5X1qamp48eIF/Pz8EBsbi+LFi8Pb21v63l+1alUcO3YMo0ePRsOGDSGEgJ2dHTp06JDrGAcNGoT4+HgMGTIET58+RaVKlfDHH3+gXLlyAN5Nzzdt2jTcvHkTampqqFWrFnbv3p3ldw11dXXMnTsX48ePx7hx49CwYUMcPXo0y3qbNGkCExMTREVFoXPnzirr3NzcsHPnTowfPx5Tp06FhoYG7O3t0aNHDwCAkZERpkyZgqCgIKSnp6NKlSrYsWMHihUrluvjQERERPQ5eNeViIiIiApToXeQunr1KpycnJCUlAQ9PT1s3boVlSpVQkREBDQ1NTM9XWlubo4nT54AAJ48eaLSOSpjfca67IwaNQpBQUHS61evXsHKyirL0XySkpLw+vVrqKurQ1290A9XjqxcuRKurq5Z3uRs3749ZsyYgcjISDRt2hQbNmzAxIkTMX36dBgYGKBRo0bSfo4fPx59+vSBvb09kpOToVQqUaVKFcyfPx9hYWGYPHky2rZtiyFDhmDp0qXSdsuWLUPv3r1Rp04dWFlZYdKkSRg2bBjU1NSgpqYmdY5RU1PL8pjmNH41NTUAUHlv3v9XXV0dbdu2xezZszFjxgwEBQXB1tYWv/32G5o2bapS7vuxfFhGxvr302bNmoXu3bvD2dkZxYsXx/Dhw5GQkACZTKayT3K5XOV1Rj0GBgY4fvw4Ro4cCR8fH7x+/RolS5aUbnYnJibi5s2b6NChA168eIESJUqgX79+6Nu3b7add3r06IFatWrhzZs3UseyJUuWICUlBR07dlTJO27cOISEhODBgwfYuXMnAKBWrVoqeQ4fPozGjRsDAMLDwzFw4EC4ublBLpfD29sbc+fOVdm3DRs2oHPnzlmOiJVx7ORyOYoVKwYtLa0s8xSmjFHRTE1N2UGqCEn4jKk5v3UZ4xqZpKTwZhIAPTOzfCm3KP7/RERE9K1asWJFtusCAwMRGBiokvbDDz9IfwcEBEijIWtqamLt2rUfratWrVrYv39/pvTsRop+v/ysyOVyBAcHIzg4OMv1PXv2RM+ePbPd/sN6e/ToIXVkypDV8ZHL5Xj06FG25bq5ucHNzS3Lda1bt5ZGQSYiIiIiIiIiIvqvkYlCnjcuJSUFMTExiI+Px6ZNm/Drr7/i2LFjiIiIQNeuXVWmwgOA2rVrw8XFBVOnTkWvXr1w79497Nu3T1r/9u1b6OrqYvfu3dIUb5/y6tUrGBoaIj4+PssOUtHR0bC1teWPpl8oY3QldXV1aXo+ylvt27dHjRo1MGrUqAKt9/nz56hQoQLOnz8PW1vbLPMU9c+SUqnE06dPYWZmxg5SRUjCkiWFHUKRoQTwQlMTxdhBCgCg16tXvpT7sTYBfRqPX/7Iz2sUR5Ci/MB2VcEr6m3toozfU79Mduce2wRfLlRWobBDoK9A8POCvf9DXydxaH1hh0BfAZlP3n83zMqSAri+9RJR+V4HEREREX2dCv1utaamJsqWLQtHR0eEhYWhWrVqmDNnDiwsLJCSkoK4uDiV/LGxsbCwsAAAWFhYIDY2NtP6jHVE/zXTp0+Hnp5egdd79+5dLFiwINvOUURERERERERERERERERERESFpdA7SH1IqVQiOTkZjo6O0NDQwKFDh6R1UVFRiImJgZOTEwDAyckJV69exdOnT6U8Bw4cgIGBASpVqlTgsRMVttKlS2PgwIEFXm/NmjXRoUOHAq+XiIiIiIiIiIiIiL4O8gJYiIiIiIiyo16YlY8aNQoeHh6wtrbG69evsWbNGhw9ehT79u2DoaEhunfvjqCgIJiYmMDAwAADBw6Ek5MT6tatCwBo3rw5KlWqhB9++AHTpk3DkydPMGbMGPTv3x8KhaIwd42IiIiIiIiIiIiIiIj+HzswEREREVFhKtQOUk+fPoWfnx8eP34MQ0NDVK1aFfv27UOzZs0AALNmzYJcLkfbtm2RnJwMNzc3LFiwQNpeTU0NO3fuRN++feHk5ARdXV34+/tj/PjxeR6rECLPyyT6L+FniIiIiIiIiIiIiIiIiIiIiApDoXaQWrZs2UfXa2lpYf78+Zg/f362eWxsbLB79+68Dk2ipqYGAEhJSYG2tna+1UP0rUtJSQHwv88UEREREREREREREf13cAQpIiIiIipMhdpB6mugrq4OHR0dPHv2DBoaGpDL2YTPLSEE0tLSoK6uDplMVtjhUAFSKpV49uwZdHR0oK7O/3aIiIiIiIiIiIiIiIiIiIio4LCnwifIZDKUKFEC0dHRuHfvXmGH81UTQkCpVEIul7OD1H+QXC6HtbU133siIiIiIiIiIiKi/yA+fk5EREREhYkdpHJAU1MT5cqVk6YIo9xRKpV48eIFihUrxpG4/oM0NTX5vhMREREREREREREREREREVGBYwepHJLL5dDS0irsML5qSqUSGhoa0NLSYkcZIiKiHDp+/DimT5+OCxcu4PHjx9i6dStat26tkuf69esYMWIEjh07hrS0NFSqVAmbN2+GtbU1ACApKQlDhgzBunXrkJycDDc3NyxYsADm5uZSGTExMejbty+OHDkCPT09+Pv7IywsjFOjEhF9xc5u7lKg9dVpu7rA6lqxYgUGDx6MuLi4AquzKAoICEBcXBy2bdtW2KEQERERfRLnFiAiIiKiwsReKkRERERF2Js3b1CtWjXMnz8/y/W3b99GgwYNYG9vj6NHj+LKlSsYO3asSsfuwMBA7NixAxs3bsSxY8fw6NEjeHt7S+vT09Ph6emJlJQUnD59GitXrsSKFSswbty4fN8/IiL67woICIBMJsu03Lp1q7BDw4oVKyCTyeDu7q6SHhcXB5lMhqNHjxZoPHfv3oVMJkNERIRK+pw5c7BixYoCjYWIiIiIiIiIiOhrxCEBiIiIiIowDw8PeHh4ZLt+9OjRaNGiBaZNmyal2dnZSX/Hx8dj2bJlWLNmDZo0aQIAWL58OSpWrIg///wTdevWxf79+xEZGYmDBw/C3NwcDg4OmDBhAkaMGIGQkBBoamrm3w4SEdF/mru7O5YvX66SZmpqWkjRqFJXV8fBgwdx5MgRuLi4FHY4WTI0NCzsEIiIiIhyjE/sExEREVFhYnuUiIiI6CulVCqxa9culC9fHm5ubjAzM0OdOnVUptm5cOECUlNT4erqKqXZ29vD2toaZ86cAQCcOXMGVapUUZlyz83NDa9evcK1a9eyrT85ORmvXr1SWTLi4pK3ixAiX8qVCZHnS2EfKy5FY8mvc5bLx4/5h0tByyqGjy0AoFAoYG5urrLI5XLMmDEDVapUga6uLqysrNC3b1+8fv060/YZf0dERMDFxQX6+vowMDCAo6Mjzp07J60/ceIEGjZsCG1tbVhZWWHgwIFISEiQysgqfl1dXXTt2hUjR47Mtl4hBGJiYuDj4wMjIyOYmJigVatWiI6OltanpqZi4MCBMDIyQrFixTB8+HD4+/ujdevWUp49e/agQYMGUp6WLVvi1q1b0npbW1sAQPXq1SGTydC4cWMIIRAQECCVs3jxYlhaWiI9PV0lvlatWqFr167S623btqFGjRrQ0tJCmTJlEBISgtTUVOlzGxwcDGtraygUClhaWmLgwIGffC+zOieJiIiIiIiIiIiKEo4gRURERPSVevr0KRISEjBlyhRMnDgRU6dOxd69e+Ht7Y0jR47A2dkZT548gaamJoyMjFS2NTc3x5MnTwAAT548UekclbE+Y112wsLCEBoamin92bNnSEpK+sK9owxKpRLx8fEQQkAuz9vnG8zT874DxdOnT/O8TPq65Nc5O+vKhTwr632BVR3zpdyClJqaCqVSibS0NKSlpUnpBd1J6v26cyKjI012282cOROlS5dGdHQ0Bg4ciGHDhmHevHnStu/X6evrCwcHB5w+fRpqamq4fPkyZDIZ0tLScPv2bXh4eCA0NBSLFy/G8+fP8eOPP6J///5YunQp0tPTAQAymUwlNgAYM2YMKlasiPXr16Nt27ZSfenp6UhLS0Nqairc3NxQt25dHD58GOrq6ggLC4O7uzsuXrwITU1NhIWFYc2aNVi6dCns7e3xyy+/YNu2bXB2dpbKe/XqFQYNGoQqVaogISEBoaGhaNOmDc6fPw+5XI7Tp0+jXr162Lt3LypVqgRNTU2kpaWpHMM2bdpg0KBBOHjwoDRq5MuXL7F371788ccfSEtLw8mTJ+Hv74+ZM2eiQYMGuHPnDvr16welUomxY8di8+bNmD17NlavXo1KlSohNjYWV65cyfY9yojhxYsX0NDQkNJfv379WecCERER/TfwiX0iIiIiKkzsIEVERET0lcr48bZVq1YIDAwEAOnH4UWLFsHZ2Tlf6x81ahSCgoKk169evYKVlRVMTU1hYGCQr3X/lyiVSshkMpiamuZ5B6lYNdmnM30mMzOzPC+Tvi75dc7mx/kKfBvnbFJSEl6/fg11dXWoq//va/77HX4Kwvt154RcLsfu3bthbGwspXl4eGDDhg0q15eyZcti4sSJ6Nu3LxYuXCht+36d9+/fx7Bhw/Ddd98BACpWrChtP336dHTu3FmlzLlz56Jx48ZYtGgR1NTUVDr3vF++tbU1Bg0ahODgYLRt21aqT01NDerq6li3bh2EEFi2bJl0vFesWAFjY2Ns3r8fDZs2wbz589E7MBA13ZoDAIZNCcPOPbuRpEzHg8S3AIDaHu5S3VoAQufNRc0ydjhy6SIqVKqEND1dAO/O11KlSqnEKZfLoa6uDlNTU3h4eGD9+vVo3vxdXdu2bUPx4sXh6uoKuVyOSZMmYcSIEejWrRsAoHz58hg/fjxGjBiB0NBQPHz4EBYWFnBzc4OGhgbKlCkDJyenbN9DdXV1yOVyFCtWDFpaWv/bh/f+JiIiIiIiIiIiKgrYQYqIiIjoK1W8eHGoq6ujUqVKKukVK1bEyZMnAQAWFhZISUlBXFycyihSsbGxsLCwkPL89ddfKmXExsZK67KjUCigUCgypWf8WEt5RyaT5ctxFfnQeYLvPQH5c87mx/kKfBvnrFwuh0wmk5bCkpu6XVxcpE5PAKCrqwuZTIaDBw8iLCwMN27cwKtXr5CWloakpCQkJiZCR0dHqivj36CgIPTs2ROrV6+Gq6sr2rdvDzs7OwDAlStXcOXKFaxZs0aqJ2NauOjoaJQrVy5T/O+XP3LkSCxZsgTLly+Hj4+PlC6TyXDlyhXcunUrU8fgpKQk3IuORrX4eDx/+hTVHGtI69TU1PBdNQcI8b9p6KJv38asSZNx+fx5/PvypdQJ+tGDB6jw3nU+u/c4I83X1xc9e/bEwoULoVAosGbNGnTs2BFqamoAgMuXL+PUqVOYPHmytG16erp0bH18fDBnzhzY2dnB3d0dLVq0gJeXV7ad3zLi+fDz/i18roiIiCjvsYVARERERIWJ7VEiIiKir5SmpiZq1aqFqKgolfR//vkHNjY2AABHR0doaGjg0KFD0vqoqCjExMRII0I4OTnh6tWrKlOjHThwAAYGBpk6XxEREeUlXV1dlC1bVlpKlCiBu3fvomXLlqhatSo2b96MCxcuYP78+QCAlJSULMsJCQnBtWvX4OnpicOHD6NSpUrYunUrACAhIQG9e/dGRESEtFy+fBk3b96UOlF9jJGREUaNGoXQ0FC8fftWZV1CQgIcHR1Vyo6IiMA///yD79u3y/Fx6NmhI+L//ReT587FlkMHseXQQQBAajb7mx0vLy8IIbBr1y7cv38fJ06cgK+vr0q8oaGhKrFevXoVN2/ehJaWFqysrBAVFYUFCxZAW1sb/fr1Q6NGjZCamvpZcRARERERERERERU1HEGKiIiIqAhLSEjArVu3pNfR0dGIiIiAiYkJrK2tMWzYMHTo0AGNGjWCi4sL9u7dix07duDo0aMAAENDQ3Tv3h1BQUEwMTGBgYEBBg4cCCcnJ9StWxcA0Lx5c1SqVAk//PADpk2bhidPnmDMmDHo379/liNEERER5acLFy5AqVRixowZ0khEGzZs+OR25cuXR/ny5REYGIhOnTph+fLlaNOmDWrUqIHIyEiULVs20zZCCKSlpX2y7IEDB2Lu3LmYM2eOSnqNGjWwfv16mJmZZRpFKvpVPACguJkZrly8iNr16wN4N2LTtSuXUalKFQDAvy9f4s7Nm5g8dw5q16sHADh35oxKWRqamtK2H6OlpQVvb2+Eh4fj1q1bqFChAmrU+N/oVTVq1EBUVFSWxyKDtrY2vLy84OXlhf79+8Pe3h5Xr15VKYeIiIgoN/jEPhEREREVJnaQIiIiIirCzp8/DxcXF+l1UFAQAMDf3x8rVqxAmzZtsGjRIoSFhWHQoEGoUKECNm/ejAYNGkjbzJo1C3K5HG3btkVycjLc3NywYMECab2amhp27tyJvn37wsnJCbq6uvD398f48eMLbkeJiIj+X9myZZGamop58+bBy8sLp06dwqJFi7LNn5iYiGHDhqFdu3awtbXFgwcPcO7cObRt2xYAMGLECNStWxcDBgxAjx49oKuri8jISBw4cADz5s3LUUxaWloIDQ1F//79VdJ9fX0xffp0tGrVCuPHj0epUqVw7949bNmyBR379kGJkiXh36sXFs6cBZsyZWBXvjxWLl6C+Lh4aVo8QyMjGJuYYN2KFTAzt8CjB/cxLSRUpZ5ipqbQ0tbG3r17UapUKWhpacHQ0DDLWH19fdGyZUtcu3YNXbp0UVk3btw4tGzZEtbW1mjXrh3kcjkuX76Mv//+GxMnTsSKFSuQnp6OOnXqQEdHB6tXr4a2trY0MiUREREREREREdHXih2kiIiIiIqwxo0bQwjx0TzdunVDt27dsl2vpaWF+fPnS9MTZcXGxga7d+/OdZxERFT01Gm7urBDyJVq1aph5syZmDp1KkaNGoVGjRohLCwMfn5+WeZXU1PDixcv4Ofnh9jYWBQvXhze3t4IDX3Xyahq1ao4duwYRo8ejYYNG0IIATs7O3To0OGz4vL398eMGTMQGRkppeno6OD48eMYMWIEvL298fr1a5QsWRJNmzaFnr4+AKB34GA8exqLIX36Qk0uR8eAADRq0gRyNTUAgFwux5zffsP4ESPg7uSEMuXKInjqVHTybCnVo66ujuCpU7Fg+nSMGzcODRs2lEaL/FCTJk1gYmKCqKgodO7cWWWdm5sbdu7cifHjx2Pq1KnQ0NCAvb09evToAeDddIJTpkxBUFAQ0tPTUaVKFezYsQPFihX7rGNFRERElBWOIEVEREREhUkmPvWL23/Aq1evYGhoiPj4+ExD4lPeUSqVePr0KczMzKRpEoiKCp6fRVPCkiWFHUKRoQTwQlMTxVJSeDMJgF6vXvlSLtsEX4bHL3/k5zXK//CePC0PAFY28cjzMunrkl/nbH6cr8C3cc4mJSUhOjoatra20NLSKuxwvioZU+ypq6tLIzrllYwp9j6kVCrRrFZteLZpjaAxYz6rTFuDrEeNKizZnXtsE3y5UFmFwg6BvgLBz0cVdgj0FRCH1hd2CPQVkPnkT1v7Q5sL4PrWVkTlex1ERERE9HXiCFJERERERERERPnkYUwMThw+gtoN6iMlORmrlizFg3v38H379oUdGhEREVGB4kN/RERERFSY2EGKiIiIiIiIiCifyORybF6zBpPHjgWEQPmKFbFq+zaUrcARgoiIiIiIiIiIiAoKO0gREREREREREeUTy1KlsHH/vsIOg4iIiKjQcQQpIiIiIipMbI8SEREREREREREREREREREREdE3iyNIERERERERERERERERUb7iE/tEREREVJjYHiUiIiIiIiIiIiIiIiIiIiIiom8WO0gRERERERERERERERFRvpIXwPK55s+fj9KlS0NLSwt16tTBX3/9lW3e1NRUjB8/HnZ2dtDS0kK1atWwd+9elTylS5eGTCbLtPTv31/K07hx40zr+/Tpk4voiYiIiOhzsIMUERERERERERERERER/aesX78eQUFBCA4OxsWLF1GtWjW4ubnh6dOnWeYfM2YMFi9ejHnz5iEyMhJ9+vRBmzZtcOnSJSnPuXPn8PjxY2k5cOAAAKB9+/YqZfXs2VMl37Rp0/JvR4mIiIgIADtIERERERERERERERERUT4raiNIzZw5Ez179kTXrl1RqVIlLFq0CDo6Ovjtt9+yzL9q1Sr89NNPaNGiBcqUKYO+ffuiRYsWmDFjhpTH1NQUFhYW0rJz507Y2dnB2dlZpSwdHR2VfAYGBp8ZPRERERF9LvXCDoCIiIiIiIiI8t7QY74FWt/PzuEFWt/XYFN4OCaMGoXLMTEfzVfG0AiLwlejecuWBRQZERER0bcpOTkZycnJKmkKhQIKhUIlLSUlBRcuXMCoUaOkNLlcDldXV5w5cybbsrW0tFTStLW1cfLkySzzp6SkYPXq1QgKCoJMJlNZFx4ejtWrV8PCwgJeXl4YO3YsdHR0cryfRERERPT5OIIUERERERERERUomUz20SUkJKTAYnF1dYVcLodMJoOWlhYqVaqEBQsW5EnZLb29cejCBen17LAweDZokCnf2X+i4NysWZ7USURERFRUyQpgCQsLg6GhocoSFhaWKZbnz58jPT0d5ubmKunm5uZ48uRJlvG7ublh5syZuHnzJpRKJQ4cOIAtW7bg8ePHWebftm0b4uLiEBAQoJLeuXNnrF69GkeOHMGoUaOwatUqdOnS5aPHjoiIiIi+HEeQIiIiIiIiIqIC9f6PSOvXr8e4ceMQFRUlpenp6Ul/CyGQnp4OdfX8u4XRo0cPTJgwAW/fvsXvv/+O/v37w9jYGJ06dfqicrW0taGlrf3JfKYf/DBHRERE9C0qiCf2R40ahaCgIJW0D0ePyq05c+agZ8+esLe3h0wmg52dHbp27ZrtlHzLli2Dh4cHLC0tVdJ79eol/V2lShWUKFECTZs2xe3bt2FnZ5cnsRIRERFRZhxBioiIiIiIiIgKlIWFhbQYGhpCJpNJr2/cuAF9fX3s2bMHjo6OUCgUOHnyJAICAtC6dWuVcgYPHozGjRtLr5VKJcLCwmBrawttbW1Uq1YNmzZt+mQ8Ojo6sLCwQJkyZRASEoJy5crhjz/+AADExMSgVatW0NPTg4GBAXx8fBAbGytte/nyZbi4uEBfXx8GBgZwdHTE+fPnAbybYq+atbX099wpU3H96t8oY2iEMoZG2BT+blrCMoZG2L9zJwCgXbPmmDIuWCW+F8+fo3yx4jh+/DiAd9O7DB06FCVLloSuri7q1KmDo0ePSvnv3bsHLy8vGBsbQ1dXF5UrV8bu3btz8M4QERERfd0UCgUMDAxUlqw6SBUvXhxqamoq7ToAiI2NhYWFRZZlm5qaYtu2bXjz5g3u3buHGzduQE9PD2XKlMmU9969ezh48CB69OjxyZjr1KkDALh161ZOdpGIiIiIcokdpIiIiIiIiIioyBk5ciSmTJmC69evo2rVqjnaJiwsDL///jsWLVqEa9euITAwEF26dMGxY8c+q25tbW2kpKRAqVSiVatWePnyJY4dO4YDBw7gzp076NChg5TX19cXpUqVwrlz53DhwgWMHDkSGhoamcps6e2NHgMGoHzFijj7TxTO/hOFlt7emfJ979MeO7dshhBCStu1ZQvMSligYcOGAIABAwbgzJkzWLduHa5cuYL27dvD3d0dN2/eBAD0798fycnJOH78OK5evYqpU6eqjMpFREREVBjkBbDklKamJhwdHXHo0CEpTalU4tChQ3BycvrotlpaWihZsiTS0tKwefNmtGrVKlOe5cuXw8zMDJ6enp+MJSIiAgBQokSJz9gDIiIiIvpcnGKPiIiIiIiIiIqc8ePHo1mzZjnOn5ycjMmTJ+PgwYPSj1plypTByZMnsXjxYjg7O3+yjPT0dKxduxZXrlxBr169cOjQIVy9ehXR0dGwsrICAPz++++oXLkyzp07h1q1aiEmJgbDhg2Dvb09AKBcuXIAgOhX8Spla2lrQ0dPF2rqah+dUs+zTRtMHDkK586cQe169QAAf2zcBK+27SCTyRATE4Ply5cjJiZGmq5l6NCh2Lt3L5YvX47JkycjJiYGbdu2RZUqVaTjQERERESqgoKC4O/vj5o1a6J27dqYPXs23rx5g65duwIA/Pz8ULJkSYSFhQEAzp49i4cPH8LBwQEPHz5ESEgIlEolhg8frlKuUqnE8uXL4e/vn2ma6Nu3b2PNmjVo0aIFihUrhitXriAwMBCNGjXK8UMBRERERJQ77CBFREREREREREVOzZo1Pyv/rVu38Pbt20ydqlJSUlC9evWPbrtw4UIsW7YMKSkpUFNTQ2BgIPr27YtffvkFVlZWUucoAKhUqRKMjIxw/fp11KpVC0FBQejRowdWrVoFV1dXtG/fHnZ2dp8V+/uKFS+OBk2aYPuGjahdrx7u372Li3/9hYmzZwEArl69ivT0dJQvX15lu+TkZBQrVgwAMGjQIPTt2xf79++Hq6sr2rZtyx/ciIiIqNAVtSlNOnTogGfPnmHcuHF48uQJHBwcsHfvXpj/f2f2mJgYyOX/izopKQljxozBnTt3oKenhxYtWmDVqlUwMjJSKffgwYOIiYlBt27dMtWpqamJgwcPSp2xrKys0LZtW4wZMyZf95WIiIiI2EGKiIiIiIiIiIogXV1dlddyuVxl2jkASE1Nlf5OSEgAAOzatQslS5ZUyadQKD5al6+vL0aPHg1tbW2UKFFC5YewTwkJCUHnzp2xa9cu7NmzB8HBwVi3bh0cmjbJcRkfauXTHuOHj0DI9Gn4Y+MmVKhcCfaVKwN4t59qamq4cOEC1NTUVLbLmEavR48ecHNzw65du7B//36EhYVhxowZGDhwYK5jIiIiIvoWDRgwAAMGDMhy3dGjR1VeOzs7IzIy8pNlNm/ePFO7NYOVldVnT/9MRERERHmjqHXYJyIiIiIiIiLKxNTUFI8fP1ZJi4iIkP6uVKkSFAoFYmJiULZsWZXl/RGgsmJgYICyZcuiZMmSKp2jKlasiPv37+P+/ftSWmRkJOLi4lCpUiUprXz58ggMDMT+/fvh7e2N5cuXZ1mPhoYm0tOVn9zXZi1aIDk5GccPHsQfmzahVXsfaV316tWRnp6Op0+fZtpPCwsLKZ+VlRX69OmDLVu2YMiQIVi6dOkn6yUiIiLKTzJZ/i9ERERERNlhBykiIiIiIiIiKvKaNGmC8+fP4/fff8fNmzcRHByMv//+W1qvr6+PoUOHIjAwECtXrsTt27dx8eJFzJs3DytXrsxVna6urqhSpQp8fX1x8eJF/PXXX/Dz84OzszNq1qyJxMREDBgwAEePHsW9e/dw6tQpnDt3DhUrVsyyvFI21nhw7x4ir1zByxcvkJycnGU+HV1dNPP0xMxJk3ArKgpe7dpK68qXLw9fX1/4+flhy5YtiI6Oxl9//YWwsDDs2rULADB48GDs27cP0dHRuHjxIo4cOZJtTERERERERERERP8FnGKPiIiIiIiI6Bv0s3N4YYeQp9zc3DB27FgMHz4cSUlJ6NatG/z8/HD16lUpz4QJE2BqaoqwsDDcuXMHRkZGqFGjBn766adc1SmTybB9+3YMHDgQjRo1glwuh7u7O+bNmwcAUFNTw4sXL+Dn54fY2FgUL14c3t7eCA0NxeOUzJ2f3L//Hvv+2IHOLb3wKj4e0xbMRztf3yzrbuXTHt3atUft+vVQ8oMRsJYvX46JEydiyJAhePjwIYoXL466deuiZcuWAID09HT0798fDx48gIGBAdzd3TFr1qxcHQMiIiKivCKXZT3tHBERERFRQZCJ7CZC/g959eoVDA0NER8fDwMDg8IO55ulVCrx9OlTmJmZqUxZQFQU8PwsmhKWLCnsEIoMJYAXmpoolpLC4R8B6PXqlS/lsk3wZXj88kd+XqP8D+/J0/IAYGUTjzwvk74u+XXO5sf5Cnwb52xSUhKio6Nha2sLLS2twg7nqyKEQFpaGtTV1SHL4zlZol/F52l5AGBrYJjnZX6J7M49tgm+XKisQmGHQF+B4OejCjsE+gqIQ+sLOwT6Csh88qet/aFjauXzvQ7n9H/yvQ4iIiIi+jpxBCkiIiIiIiIiIiIiIiLKV3ncH52IiIiI6LNwEAoiIiIiIiIiIiIiIiIiIiIiIvpmcQQpIiIiIiIiIiIiIiIiylccQIqIiIiIChM7SBEREREREREREX0BIQQuXLiAu3fvQiaTwdbWFtWrV4eMcwkRERERERERERUJ7CBFRERERERERESUS0eOHEH37t1x7949CCEAQOok9dtvv6FRo0aFHCEREVHRIJOJwg6BiIiIiP7D5IUdABERERERERER0dfo1q1baNmyJUqXLo0tW7bg+vXriIyMxMaNG1GqVCm0aNECd+7cKewwiYiIiIiIiIj+8ziCFBERERERERERUS7Mnj0bdevWxaFDh1TS7e3t0aZNG7i6umLWrFmYN29eIUVIRERUdHDmWSIiIiIqTBxBioiIiIiIiIiIKBeOHj2KwYMHZ7lOJpNh8ODBOHLkSMEGRUREREREREREmXAEKSIiIiIiIiIiolyIiYlBlSpVsl3/3Xff4d69ewUYERERUdHFEaSIiIiIqDCxgxQRERERERHRN8j/8J4CrW9lE48Cre+/poyhEbZu3YrWrVsXdij0noSEBOjo6GS7XkdHB2/fvi3AiIiIiIiIiIiIKCvsIEVEREREREREBUr2ieEDgoODERISUiCxuLq64vjx41i7di06duwopc+ePRuzZ8/G3bt3CyQOqd6wMBzYtQu7Tp5UST/7TxSqWdsUaCyUM5GRkXjy5EmW654/f17A0RQ91g1rot6w7rB0/A76lmZY17oforYf+ug2Ns614TZzJEwrl8Or+49xfOJCXF65VSVPrX6dUW9Yd+hZmOLJ5RvYM3ACHp27Kq1XU2jCbcZIVO7YAuoKTdzadxK7+4XizdMX+bKf9OXCN1/AsvCzePYyAfZlzTA2qDmqVrLMNv+K9X9h7dZLePzkFYyNtOHmYo8hfRpDoVDPcZkxD/7F1F8O48KV+0hJSUfDumUwNqg5ipvo5uu+Uu6Fn43HspPxeJ6QDnsLTYzxLIaqpbSyzb/ydDzW/vUKj+PTYKwjh1tlXQQ1M4FCQy7liX2Vhp/3vcTxm2+RlCpgbaKOyd5mqFJSAQB4npCGn/e/xKlbiXidpERNGy2MaVkcpYtp5Pv+fmvkMlHYIRARERHRf5j801mIiIiIiIiIiPLO48ePpWX27NkwMDBQSRs6dKiUVwiBtLS0fI1HS0sLY8aMQWpqar7W8yVMzc2hUCgKOwzKQtOmTeHg4JBpqV69OlxdXQs7vEKnqauD2MtR2N0/NEf5jUqXQuddi3H3yFksdmiFP2evxPe/ToRd8wZSnso+Hmg+cxSOhc7H4hptEHv5BrrsWwYdUxMpj/usn1DeywUb2w/GCucfoG9pBp8tv+T5/lHe2H0wEmFzD6F/twbYurwb7Muao3vgerx4+SbL/Dv2X8OMhUcxoFsD7F7bE5NGtcDuQ9cxc9HRHJf5NjEF3Qavg0wGrJzXGWsX/4DU1HT0GbYRSiU7cRRFu68mYMqeF+jvYowtfUuigoUmeqx8ghcJ6Vnm33E5ATMOvER/F2PsGlQKE9uYYvffbzDz4L9SnvjEdHRa+gjqasBSPwvsGlQKIzyKwVD73U8nQgj0XxOLBy/TsKCzObb0LQlLI3V0W/4Yb1OUBbLfRERERESUN9hBioiIiIiIiIgKlIWFhbQYGhpCJpNJr2/cuAF9fX3s2bMHjo6OUCgUOHnyJAICAjJNLzd48GA0btxYeq1UKhEWFgZbW1toa2ujWrVq2LRp0yfj6dixI+Li4rB06dKP5tu+fTtq1KgBLS0tlClTBqGhoSqdt27cuIEGDRpAS0sLzWvXwckjR1HG0Aj7d+6U8kwZF4wmNRxRyaIEnKtWw8yJE6WOWZvCwzF3ylRcv/o3yhgaoYyhETaFhwN4N8Xetm3bAAD16tXDiBEjVGJ79uwZNDQ0cPz4cQBAcnIyhg4dipIlS0JXVxd16tTB0aNHpfz37t2Dl5cXjI2Noauri8qVK2P37t2fPFakKjo6Gnfu3EF0dHSmJSP9zp07hR1mobq19ziOjJ2NG9sO5ih/zT4dERf9APuHTsXzG3dwbn44IjftQ93AAClP3aCuuLh0AyJWbMHz67exs08wUt8moXq3tgAAhYEeqndvi31BU3D3yJ94fPEatnf9Cdb1a6BknWr5sZv0hZav+ws+31dD25ZVUda2OEKHu0NLoY7NO69kmf/S1QeoUaUUvJpXRqkSRmhQpwxaulbCleuPc1zmxSsP8PBJPKaMaYkKdmaoYGeGqWNb4u8bj/HnhbsFsdv0mVacjkf7mgZoW0MfZc00EepVHFoaMmy++DrL/JfuJ6GGtQJe1fRQylgDDcrqwLOKHq4+SJLy/HoiDiUM1RHmbYaqpbSkfNYm70aHuvsiFZfvJyPYqziqlNJCGVNNhHgVR1KaEruuJBTIfn9LZAWwEBERERFlh1PsEREREREREVGRM3LkSPz8888oU6YMjI2Nc7RNWFgYVq9ejUWLFqFcuXI4fvw4unTpAlNTUzg7O2e7nYGBAUaPHo3x48fD398furqZp1Y6ceIE/Pz8MHfuXDRs2BC3b99Gr169ALybEjA9PR2tW7eGtbU1zp49i6gnjzF59JhM5ejp62H6wgUwt7BAVGQkRg36Ebp6+ug9+Ee09PbGP5HXcfzQIazavg0AoG9gkKkMX19fTJs2DVOmTJGmK1y/fj0sLS3RsGFDAMCAAQMQGRmJdevWwdLSElu3boW7uzuuXr2KcuXKoX///khJScHx48ehq6uLyMhI6Onp5eg40//Y2Hx62sO///67ACL5dpRycsCdg2dU0m7vOwm32T8BAOQaGrB0rIyTYYv/l0EI3Dl4GqWcqgMASjh+BzVNTdw5eFrK8iLqDuLuPYSVkwMenr2c/ztCOZaSmo5rUU/Q+4d6UppcLkO9WqVx6e+HWW5TvUop/LHvGq5EPkLVSpa4//BfHDtzG63cv8txmSmp6ZDJAE0NNSmPQlMdcrkMFy4/QL1atvmxu5RLKWkC1x4lo1dDIylNLpfByU4bEfeTstymupUWdlxOwJUHSahaSgv3X6bi+D9v8b3D/653h2+8RYOy2vhxXSzO3U2Eub46OtUxgE9Ng/+v910+hcb/ut7I5TJoqslwISYJ7Wtmvk4TEREREVHRxA5SRERERERERFTkjB8/Hs2aNctx/uTkZEyePBkHDx6Ek5MTAKBMmTI4efIkFi9e/NEOUgDQr18/zJkzBzNnzsTYsWMzrQ8NDcXIkSPh7+8vlT1hwgQMHz4cwcHBOHDgAG7fvo2jR4/CwsICBralMWTsWPh9MOrVgGHDpL9L2dig58Bb2LF5M3oP/hFa2trQ0dOFmroaTM3Ns43Vx8cHgwcPxsmTJ6UOUWvWrEGnTp0gk8kQExOD5cuXIyYmBpaWlgCAoUOHYu/evVi+fDkmT56MmJgYtG3bFlWqVJH2h/LO69evsXbtWvz666+4cOEC0tOznv4JeHfuJicnq6SlQQn1/+jA73oWxfEm9rlKWkLsc2gZ6kNdSwEtY0PI1dXxJvaFSp43sS9Q3L6MVEZacgqS419nyqNnYZq/O0Cf7d+4t0hPFyhmoqOSXsxEF3fuvchyG6/mlfFv3Ft07rMKQgBp6Up0bFMdffzr5bhMh8oloa2liekLjiCoT2MIITBj4VGkpws8e8GRgYqaf9+mI10JFNNTU0kvrqeG6OdZT5HrVU0P/75Nh++vj96dJ0qgYy199HH+X8fr+/+mYe251wioZ4jejYxw9WEyJu16AQ01GdpU10cZUw1YGqpj5v6XCG1VHNoacqw8HY8nr9Lx7HX2/7dT1mQc4omIiIiIChE7SBERERERERFRkVOzZs3Pyn/r1i28ffs2U6eqlJQU/B979x0dVbX2cfx3JpWWQCCFYKgqvQkaUESUSCAIotyLIAgCgiARJSoSpasEUEKTKxaKBRTvBXm9oEgHkUgJBqSIUiMlIbQEAqkz7x9cBsckkIEZhpDvZ62zVs4+z9nnOcPOOOY8s3fjxo2veb6Xl5fGjh2rF198UQMHDsxzfPv27frpp5/0zjvvWNtyc3OVkZGhCxcuaO/evQoJCVFQUJD1eMMm9+TpZ8nCRfr0ww91+OBBXUhPV05OjsqUKWPPrcrf319t2rTRvHnz9OCDD+rgwYOKi4vThx9emlHn119/VW5uru6++26b8zIzM1W+fHlJ0uDBgzVw4EAtX75cYWFh6ty5sxo0aGBXHshr/fr1mjVrlhYuXKjg4GA9+eSTmjFjxlXPiYmJ0ZgxY2zaHpKfHlYFZ6YKFGmbth3Wh5/FadSr4WpQN1iJR87onSkrNWPOBg3q3aJQffiVK6mpb3fS6Hd/0Of/3iqTyVD7sDqqWzNIhokqjtvBpoMX9dH6sxr5WAU1uMNbiaezNe67k/rXmjN64eFLRVIWi0V1g70U9aifJKlOsJf+OJGlr7ak6YnGZeThZmhat0ANX5yi0HGH5WaSmlcvoZZ3lZDFlTcHAAAAwG4USAEAAAAAgFvO35e5M5lMslhsH0VmZ1+ZMeL8+UuzfSxdulSVKlWyifPy8irUNXv06KH33ntPb7/9tqpWrWpz7Pz58xozZoyefPLJPOd5e3sXqv9tmzdrSL9+ejk6Wg+2bq0yvj5asnChPnn//UKd/1fdu3fX4MGDNX36dM2fP1/169e3zgZ1/vx5ubm5KT4+Xm5utjNtXF5G77nnnlN4eLiWLl2q5cuXKyYmRpMmTdKLL75ody7FXVJSkubOnatZs2YpLS1NXbp0UWZmphYvXqw6depc8/zo6GhFRUXZtL3r28RZ6d7yziedVKlA2+Kw0oEVlJF6TjkZmbpw8ozMOTkqFVjeJqZUYHmdTzpp7cPdy1NevmVsZpG6FJPi/JuAXcqVLSk3N0OnTl+waT91Ol0V/PJf+nPqx+vVsW09/bNjI0lSzRoBunAxWyMnfK+BvR4odJ8tQqtr5X8G6vTZC3J3M8mnjLceeGyaIoJrO/YmccPKlXSTm0k6dd521qaT53NV4W+zSl02bdUZdWxY2roMXs0gT13MMmvktyc14KGyMpkM+Zd2150Bnjbn1fD31PJd6db9epW8tHjQHTqXYVZ2rkV+pdzU5cOjqhdcuM8XuIIZpAAAAOBKxXOubgAAAAAAUKT4+/vr+PHjNm0JCQnWn+vUqSMvLy8lJibqzjvvtNlCQkIKdQ2TyaSYmBh98MEHOnTokM2xe+65R3v37s3T95133imTyaSaNWvqzz//VHJysvWcHdt+selj26ZNqhQSokGvvaoG9zRWtRo1dDTxT5sYDw9P5eaar5nr448/royMDC1btkzz589X9+7drccaN26s3NxcnThxIk+uf53hKiQkRAMGDNCiRYv0yiuv6OOPPy7U64QrOnTooJo1a2rHjh2aMmWKjh07punTp9vVh5eXl3x8fGy24rq8niQdiUtQtdbNbNqqP3q/jsQlSJLM2dk6Fr9L1Vs3vxJgGKreurmOxF36nTsev1O5WVk2MeXvrqayVSrpz//1g1uHp4eb6tYMUlz8IWub2WxR3NbDalyvUr7nZGTkyPS3Sgu3/836ZLFY7O7Tr2xJ+ZTxVtzWQzp1Jl2PtLjrxm8MDuXpbqhusJfiDly0tpnNFv184KIaheRfqHwx26y/TwZmujxO/rffuLJXniX6Dp3MUnDZvN8tL+Ntkl8pNx06la2dRzP1SO2SeWIAAAAA3LqYQQoAAAAAANzyHnnkEb377rv67LPP1Lx5c33xxRfauXOndfm8MmXK6NVXX9WQIUNkNpvVokULpaam6qeffpKPj4969epVqOu0b99eoaGh+vDDDxUYGGhtHzlypB577DFVrlxZ//jHP2QymbR9+3bt3LlTb7/9th599FHVqFFDvXr10sSJE7U36bhi335bkmT87yF+1Ro1dOzIEf33PwvV4J57tGb5D1q+ZInN9e+oUllHDh/W7h07FFSpkkqVLp3vDFilSpVSp06dNGLECO3Zs0fdunWzHrv77rvVvXt39ezZU5MmTVLjxo2VkpKiVatWqUGDBmrfvr1efvlltWvXTnfffbfOnDmjNWvWqHZtZkyx1/fff29drvCuuyioyI9HqZLyu7Oydb9ctTsU2LCWLp5OVdqfx9V6XJTKVArU4l6vS5K2zvxK90Z2V9iE1/TL7IWq9kgz1e3STvPbP2/t4+fYOer06QQd27pTRzfvULOXe8mjVAklzFkkScpMO69fZi1Um9hhung6VZlp59Vu+nD9uXGbjm7afnNfABRK76736fW3l6herSA1qBOsTxds0cWMbD352KWlP4eO/a8C/cvolYGtJEkPP3Cn5ny1WXXuDrQusTf14/V6uMVdcnMzFapPSVq4ZIdqVC0vv7Il9cvOoxo3ZYWefeo+Va9SPk+OcL1n7/fVsEUpqlfJSw0qeenTuFRdzLLoyXsuzQr2+n9OKMDHXa+0ubRc3sM1S2ruxlTVruilhiFeOnwqW9NWndbDNUtaC+qevd9X3T4+ppnrzqhdvdLacSRTX289p7GPX5nJbtnO8ypXyk3Bvu76PTlL73x3Sq1rl1SLOymQspdhsDAhAAAAXIcCKQAAAAAAbkOfPtLO1Sk4VHh4uEaMGKGhQ4cqIyNDffr0Uc+ePfXrr79aY9566y35+/srJiZGBw4cUNmyZXXPPffojTfesOtaEyZM0P3335/n+kuWLNHYsWM1YcIEeXh4qFatWnruueckSW5ublq8eLGee+453XvvvQqpWlXD3hqrfk91lZfXpZktwiIi1OeFFzT6tdeUlZWlh9u0UeTQ1zR1/Hjrddp27Kgfvv2vnn6sg9JSUzXxXzP0j7/MDvVX3bt3V0REhFq2bKnKlSvbHJszZ47efvttvfLKKzp69KgqVKigZs2a6bHHHpMk5ebmatCgQTpy5Ih8fHzUtm1bTZ482a7XCdKGDRs0a9YsNWnSRLVr19Yzzzyjrl27ujqtW0pw03p6du3n1v3wyZd+HxPmLtL/9Y5W6Yr+8q1c0Xr87KEjmt/+eYVPjlboSz2VdiRJ3z43XPuXb7DG7Pr6e5X091OrsYNVOshfSQl7NK/tc0o/ccoas2zIOIWbzeqycJrcvDy1/4cNWvrCmJtwx7geEWF1dPrsBU37+EelnE5X7bsC9ElsF1Xwu7Tc6vHkNOvMP5I08NkHZBjSlI/WKTnlvPzKldTDD9ypIc8/VOg+Jelg4inFzlyr1LSLqlTRVwN6PaBnu957824cdomoX1qn03M1fdUZpZzPUe2KXvq4Z5AqlL70mONYao6Mv0zAN/ChcjJkaOqq00pOy5VfKZMerllKL4eVs8bUv8Nb058OVOzy0/rX2rO6o6y7oiPKq0PDMtaYE+dyNf77UzqVniv/0u56vFFpDWx1pQ8U3t9n9AIAAABuJsNisRT7kv20tDT5+voqNTVVPj4+rk7ntmU2m3XixAkFBATIZCq+U8Xj1sT4vDWd/+gjV6dwyzBLOuXpqfJZWcV4sY0rSvfv75R++UxwY3j9nMOZ/43qtfp7h/Yn3X4FGbCfs8asM8ardHuM2YyMDB08eFDVqlWTt3f+S8wgfxaLRTk5OXJ3d7fO8uQoB9NStfXnn9UlvK3W/PKLqlSvdsN9VvPxdUBmjlPQ2CuOnwnS09O1YMECzZ49W5s3b1Zubq5iY2PVp08flSlT5tod/M0Yo6YTssTtZtTJaFengCLAsmqBq1NAEWB0cc5n7b/bVrqG069xz/n9Tr8GAAAAiiaesQIAAAAAADjAN998oxUrVujQoUPasGat3nzpZTVp1swhxVG4tZUqVUp9+vTRhg0b9Ouvv+qVV17R+PHjFRAQoI4dO7o6PQAAbgmG4fwNAAAAKAgFUgAAAAAAAA5w7tw5DRo0SLVq1dLQF15Qg3sa66Mv57s6LdxkNWvW1MSJE3XkyBF99dVXDp+dDAAAAAAAAPZzd3UCAAAAAAAAt4OePXuqZ8+eki4tsYfbX58+fa4ZU758+ZuQCQAAtz5DFlenAAAAgGKMGaQAAABucevXr1eHDh0UHBwswzC0ePHiAmMHDBggwzA0ZcoUm/bTp0+re/fu8vHxUdmyZdW3b1+dP3/eJmbHjh168MEH5e3trZCQEE2cONEJdwMAAHD7mDt3rtasWaOzZ8/qzJkz+W5nz551dZoAAAAAAADFHjNIAQAA3OLS09PVsGFD9enTR08++WSBcd98841+/vlnBQcH5znWvXt3HT9+XCtWrFB2drZ69+6t/v37a/78S8v+pKWlqU2bNgoLC9PMmTP166+/qk+fPipbtqz69+/vtHsDADiOxcI38nFzmc1mV6fgcgMHDtSXX36pgwcPqnfv3urRo4f8/PxcnRYAALckVp0FAACAK1EgBQAAcItr166d2rVrd9WYo0eP6sUXX9QPP/yg9u3b2xzbs2ePli1bpi1btqhp06aSpOnTpysiIkLvvfeegoODNW/ePGVlZWn27Nny9PRU3bp1lZCQoNjYWAqkAOAW5+HhIcMwlJKSIn9/fxk8eSo0i8WinJwcubu7O/x1y83Kcmh/kpSRkeHwPq+HxWJRVlaWUlJSZDKZ5Onp6eqUXGbGjBmKjY3VokWLNHv2bEVHR6t9+/bq27ev2rRpw+8jAAAAAADALYICKQAAgCLObDbrmWee0Wuvvaa6devmOR4XF6eyZctai6MkKSwsTCaTSZs2bdITTzyhuLg4tWzZ0uYBZ3h4uCZMmKAzZ86oXLlyefrNzMxUZmamdT8tLc2aDzNKOI7ZbJbFYnHKa2o4YbYZ/u3hrDHrjPEq3R5j1jAMVapUSUePHtXBgwddnU6RYzabZTKZHN7vqUzHFzNleXk7vM8bUbJkSd1xxx2SbH+XboffK3t4eXmpW7du6tatmw4fPqy5c+fqhRdeUE5Ojnbt2qXSpUu7OkUAAG4J1A0DAADAlSiQAgAAKOImTJggd3d3DR48ON/jSUlJCggIsGlzd3eXn5+fkpKSrDHVqlWziQkMDLQey69AKiYmRmPGjMnTnpKScsvMcHE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      "text/plain": [
       "<Figure size 2400x1200 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create comprehensive performance visualization\n",
    "fig, axes = plt.subplots(2, 4, figsize=(24, 12))\n",
    "\n",
    "# 1. Performance metrics comparison\n",
    "metrics = ['Precision', 'Recall', 'F1-Score', 'AUC']\n",
    "x = np.arange(len(models))\n",
    "width = 0.2\n",
    "\n",
    "for i, metric in enumerate(metrics):\n",
    "    axes[0, 0].bar(x + i*width, performance_df[metric], width, label=metric, alpha=0.8)\n",
    "\n",
    "axes[0, 0].set_xlabel('Models')\n",
    "axes[0, 0].set_ylabel('Score')\n",
    "axes[0, 0].set_title('Performance Metrics Comparison')\n",
    "axes[0, 0].set_xticks(x + width*1.5)\n",
    "axes[0, 0].set_xticklabels(performance_df['Model'])\n",
    "axes[0, 0].legend()\n",
    "axes[0, 0].grid(True, alpha=0.3)\n",
    "\n",
    "# 2. Confusion matrix comparison (fixed indexing)\n",
    "for i, (name, predictions) in enumerate(models.items()):\n",
    "    cm = confusion_matrix(y_aligned, predictions)\n",
    "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', ax=axes[0, i+1])\n",
    "    axes[0, i+1].set_title(f'Confusion Matrix - {name}')\n",
    "    axes[0, i+1].set_xlabel('Predicted')\n",
    "    axes[0, i+1].set_ylabel('Actual')\n",
    "\n",
    "# 3. Anomaly detection count comparison\n",
    "detection_counts = [predictions.sum() for predictions in models.values()]\n",
    "axes[1, 0].bar(performance_df['Model'], detection_counts, alpha=0.8, color='lightcoral')\n",
    "axes[1, 0].axhline(y=y_aligned.sum(), color='red', linestyle='--', \n",
    "                   label=f'Actual Anomalies ({y_aligned.sum()})')\n",
    "axes[1, 0].set_xlabel('Models')\n",
    "axes[1, 0].set_ylabel('Number of Anomalies Detected')\n",
    "axes[1, 0].set_title('Anomaly Detection Count Comparison')\n",
    "axes[1, 0].legend()\n",
    "axes[1, 0].grid(True, alpha=0.3)\n",
    "\n",
    "# 4. Error type comparison\n",
    "error_types = ['False Positives', 'False Negatives', 'True Positives', 'True Negatives']\n",
    "error_data = []\n",
    "for name in performance_df['Model']:\n",
    "    row = performance_df[performance_df['Model'] == name].iloc[0]\n",
    "    error_data.append([row['FP'], row['FN'], row['TP'], row['TN']])\n",
    "\n",
    "error_df = pd.DataFrame(error_data, columns=error_types, index=performance_df['Model'])\n",
    "error_df.plot(kind='bar', ax=axes[1, 1], alpha=0.8)\n",
    "axes[1, 1].set_title('Error Type Comparison')\n",
    "axes[1, 1].set_xlabel('Models')\n",
    "axes[1, 1].set_ylabel('Count')\n",
    "axes[1, 1].legend()\n",
    "axes[1, 1].grid(True, alpha=0.3)\n",
    "\n",
    "# 5. Model agreement analysis\n",
    "agreement_matrix = np.zeros((len(models), len(models)))\n",
    "model_names = list(models.keys())\n",
    "\n",
    "for i, name1 in enumerate(model_names):\n",
    "    for j, name2 in enumerate(model_names):\n",
    "        pred1 = models[name1]\n",
    "        pred2 = models[name2]\n",
    "        agreement = (pred1 == pred2).mean()\n",
    "        agreement_matrix[i, j] = agreement\n",
    "\n",
    "sns.heatmap(agreement_matrix, annot=True, fmt='.3f', cmap='YlOrRd', \n",
    "            xticklabels=model_names, yticklabels=model_names, ax=axes[1, 2])\n",
    "axes[1, 2].set_title('Model Agreement Matrix')\n",
    "axes[1, 2].set_xlabel('Models')\n",
    "axes[1, 2].set_ylabel('Models')\n",
    "\n",
    "# Hide the unused subplot\n",
    "axes[1, 3].set_visible(False)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error Analysis Summary:\n",
      "\n",
      "MAD Model:\n",
      "  False Positives: 128 (0.064)\n",
      "  False Negatives: 121 (0.061)\n",
      "  True Positives: 99 (0.050)\n",
      "  True Negatives: 1651 (0.826)\n",
      "\n",
      "VAR Model:\n",
      "  False Positives: 179 (0.090)\n",
      "  False Negatives: 199 (0.100)\n",
      "  True Positives: 21 (0.011)\n",
      "  True Negatives: 1600 (0.800)\n",
      "\n",
      "Fourier Model:\n",
      "  False Positives: 185 (0.093)\n",
      "  False Negatives: 105 (0.053)\n",
      "  True Positives: 115 (0.058)\n",
      "  True Negatives: 1594 (0.797)\n"
     ]
    }
   ],
   "source": [
    "# Analyze false positives and false negatives for each model\n",
    "error_analysis = {}\n",
    "\n",
    "for name, predictions in models.items():\n",
    "    false_positives = (predictions == 1) & (y_aligned == 0)\n",
    "    false_negatives = (predictions == 0) & (y_aligned == 1)\n",
    "    true_positives = (predictions == 1) & (y_aligned == 1)\n",
    "    true_negatives = (predictions == 0) & (y_aligned == 0)\n",
    "    \n",
    "    error_analysis[name] = {\n",
    "        'false_positives': false_positives,\n",
    "        'false_negatives': false_negatives,\n",
    "        'true_positives': true_positives,\n",
    "        'true_negatives': true_negatives\n",
    "    }\n",
    "\n",
    "print(\"Error Analysis Summary:\")\n",
    "for name in models.keys():\n",
    "    fp_count = error_analysis[name]['false_positives'].sum()\n",
    "    fn_count = error_analysis[name]['false_negatives'].sum()\n",
    "    tp_count = error_analysis[name]['true_positives'].sum()\n",
    "    tn_count = error_analysis[name]['true_negatives'].sum()\n",
    "    \n",
    "    print(f\"\\n{name} Model:\")\n",
    "    print(f\"  False Positives: {fp_count} ({fp_count/len(y_aligned):.3f})\")\n",
    "    print(f\"  False Negatives: {fn_count} ({fn_count/len(y_aligned):.3f})\")\n",
    "    print(f\"  True Positives: {tp_count} ({tp_count/len(y_aligned):.3f})\")\n",
    "    print(f\"  True Negatives: {tn_count} ({tn_count/len(y_aligned):.3f})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Visual Anomaly Comparison Summary:\n",
      "Green triangles (^): Actual anomalies\n",
      "Red squares (s): Predicted anomalies\n",
      "Orange X: False positives (normal classified as anomaly)\n",
      "Purple X: False negatives (anomaly missed)\n",
      "Blue dots: Normal samples\n"
     ]
    }
   ],
   "source": [
    "# Create visual comparison of actual vs predicted anomalies\n",
    "fig, axes = plt.subplots(3, 1, figsize=(15, 12))\n",
    "\n",
    "sample_range = slice(0, 200)  # First 200 samples for visualization\n",
    "y_vis = y_aligned[sample_range]\n",
    "\n",
    "for i, (name, predictions) in enumerate(models.items()):\n",
    "    pred_vis = predictions[sample_range]\n",
    "    \n",
    "    # Plot actual anomalies in green\n",
    "    actual_anomaly_indices = np.where(y_vis == 1)[0]\n",
    "    axes[i].scatter(actual_anomaly_indices, [1.2] * len(actual_anomaly_indices), \n",
    "                    color='green', s=50, marker='^', label='Actual Anomalies', alpha=0.8)\n",
    "    \n",
    "    # Plot predicted anomalies in red\n",
    "    predicted_anomaly_indices = np.where(pred_vis == 1)[0]\n",
    "    axes[i].scatter(predicted_anomaly_indices, [0.8] * len(predicted_anomaly_indices), \n",
    "                    color='red', s=50, marker='s', label='Predicted Anomalies', alpha=0.8)\n",
    "    \n",
    "    # Plot normal samples in blue\n",
    "    normal_indices = np.where(y_vis == 0)[0]\n",
    "    axes[i].scatter(normal_indices, [1.0] * len(normal_indices), \n",
    "                    color='blue', s=20, alpha=0.3, label='Normal Samples')\n",
    "    \n",
    "    # Highlight false positives and false negatives\n",
    "    false_positives = (pred_vis == 1) & (y_vis == 0)\n",
    "    false_negatives = (pred_vis == 0) & (y_vis == 1)\n",
    "    \n",
    "    fp_indices = np.where(false_positives)[0]\n",
    "    fn_indices = np.where(false_negatives)[0]\n",
    "    \n",
    "    axes[i].scatter(fp_indices, [0.8] * len(fp_indices), \n",
    "                    color='orange', s=100, marker='x', label='False Positives', alpha=0.9)\n",
    "    axes[i].scatter(fn_indices, [1.2] * len(fn_indices), \n",
    "                    color='purple', s=100, marker='x', label='False Negatives', alpha=0.9)\n",
    "    \n",
    "    axes[i].set_title(f'{name} Model - Anomaly Detection (First 200 samples)')\n",
    "    axes[i].set_xlabel('Sample Index')\n",
    "    axes[i].set_ylabel('Detection Type')\n",
    "    axes[i].legend()\n",
    "    axes[i].grid(True, alpha=0.3)\n",
    "    axes[i].set_ylim(0.5, 1.5)\n",
    "    \n",
    "    # Add performance metrics\n",
    "    tp = ((pred_vis == 1) & (y_vis == 1)).sum()\n",
    "    fp = ((pred_vis == 1) & (y_vis == 0)).sum()\n",
    "    fn = ((pred_vis == 0) & (y_vis == 1)).sum()\n",
    "    tn = ((pred_vis == 0) & (y_vis == 0)).sum()\n",
    "    \n",
    "    precision = tp / (tp + fp) if (tp + fp) > 0 else 0\n",
    "    recall = tp / (tp + fn) if (tp + fn) > 0 else 0\n",
    "    \n",
    "    axes[i].text(0.02, 0.98, f'Precision: {precision:.3f}\\nRecall: {recall:.3f}', \n",
    "                 transform=axes[i].transAxes, verticalalignment='top', \n",
    "                 bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.8))\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"Visual Anomaly Comparison Summary:\")\n",
    "print(\"Green triangles (^): Actual anomalies\")\n",
    "print(\"Red squares (s): Predicted anomalies\")\n",
    "print(\"Orange X: False positives (normal classified as anomaly)\")\n",
    "print(\"Purple X: False negatives (anomaly missed)\")\n",
    "print(\"Blue dots: Normal samples\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== FINAL RECOMMENDATIONS AND CONCLUSIONS ===\n",
      "\n",
      "1. BEST PERFORMING MODEL: MAD (F1-Score: 0.4430)\n",
      "\n",
      "2. MODEL STRENGTHS:\n",
      "   MAD: Fast, interpretable, robust to outliers\n",
      "   VAR: Captures temporal dependencies, good for time series\n",
      "   Fourier: Robust to noise, detects frequency-based anomalies\n",
      "\n",
      "3. ERROR ANALYSIS SUMMARY:\n",
      "   False Positives: Normal signals with unusual patterns\n",
      "   False Negatives: Subtle anomalies that don't trigger thresholds\n",
      "   Critical errors: False negatives in medical applications\n",
      "\n",
      "4. RECOMMENDATIONS FOR REAL-WORLD APPLICATIONS:\n",
      "   - Use ensemble approach combining all three models\n",
      "   - Tune thresholds based on medical requirements\n",
      "   - Implement real-time monitoring with alert systems\n",
      "   - Regular model retraining with new data\n",
      "   - Human expert validation for critical decisions\n",
      "\n",
      "5. PROJECT COMPLETION:\n",
      "   - All three models implemented and evaluated\n",
      "   - Comprehensive error analysis completed\n",
      "   - Visual comparisons created\n",
      "   - Results saved for presentation\n",
      "   - Ready for defense presentation!\n"
     ]
    }
   ],
   "source": [
    "# Generate final recommendations\n",
    "print(\"=== FINAL RECOMMENDATIONS AND CONCLUSIONS ===\")\n",
    "\n",
    "# Find best performing model\n",
    "best_model = performance_df.loc[performance_df['F1-Score'].idxmax(), 'Model']\n",
    "best_f1 = performance_df['F1-Score'].max()\n",
    "\n",
    "print(f\"\\n1. BEST PERFORMING MODEL: {best_model} (F1-Score: {best_f1:.4f})\")\n",
    "\n",
    "# Model strengths analysis\n",
    "print(\"\\n2. MODEL STRENGTHS:\")\n",
    "print(\"   MAD: Fast, interpretable, robust to outliers\")\n",
    "print(\"   VAR: Captures temporal dependencies, good for time series\")\n",
    "print(\"   Fourier: Robust to noise, detects frequency-based anomalies\")\n",
    "\n",
    "# Error analysis summary\n",
    "print(\"\\n3. ERROR ANALYSIS SUMMARY:\")\n",
    "print(\"   False Positives: Normal signals with unusual patterns\")\n",
    "print(\"   False Negatives: Subtle anomalies that don't trigger thresholds\")\n",
    "print(\"   Critical errors: False negatives in medical applications\")\n",
    "\n",
    "# Recommendations\n",
    "print(\"\\n4. RECOMMENDATIONS FOR REAL-WORLD APPLICATIONS:\")\n",
    "print(\"   - Use ensemble approach combining all three models\")\n",
    "print(\"   - Tune thresholds based on medical requirements\")\n",
    "print(\"   - Implement real-time monitoring with alert systems\")\n",
    "print(\"   - Regular model retraining with new data\")\n",
    "print(\"   - Human expert validation for critical decisions\")\n",
    "\n",
    "# Save comprehensive results\n",
    "comprehensive_results = {\n",
    "    'performance_comparison': performance_df,\n",
    "    'error_analysis': error_analysis,\n",
    "    'best_model': best_model,\n",
    "    'recommendations': {\n",
    "        'best_model': best_model,\n",
    "        'best_f1_score': best_f1,\n",
    "        'ensemble_recommendation': True,\n",
    "        'medical_criticality': 'High - False negatives are critical'\n",
    "    }\n",
    "}\n",
    "\n",
    "with open('../results/comprehensive_comparison.pkl', 'wb') as f:\n",
    "    pickle.dump(comprehensive_results, f)\n",
    "\n",
    "print(\"\\n5. PROJECT COMPLETION:\")\n",
    "print(\"   - All three models implemented and evaluated\")\n",
    "print(\"   - Comprehensive error analysis completed\")\n",
    "print(\"   - Visual comparisons created\")\n",
    "print(\"   - Results saved for presentation\")\n",
    "print(\"   - Ready for defense presentation!\")"
   ]
  },
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   "source": []
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   "cell_type": "code",
   "execution_count": null,
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